diff --git "a/7tE1T4oBgHgl3EQfTwM_/content/tmp_files/load_file.txt" "b/7tE1T4oBgHgl3EQfTwM_/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/7tE1T4oBgHgl3EQfTwM_/content/tmp_files/load_file.txt" @@ -0,0 +1,877 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf,len=876 +page_content='1 Abstract— Objective: The objective of this study is to develop a deep-learning based detection and diagnosis technique for carotid atherosclerosis using a portable freehand 3D ultrasound (US) imaging system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Methods: A total of 127 3D carotid artery datasets were acquired using a portable 3D US imaging system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' A U-Net segmentation network was firstly applied to extract the carotid artery on 2D transverse frame, then a novel 3D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Furthermore, a convolutional neural network was used to classify the healthy case and diseased case qualitatively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3D volume analysis including longitudinal reprojection algorithm and stenosis grade measurement algorithm was developed to obtain the clinical metrics quantitatively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Results: The proposed system achieved sensitivity of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='714, specificity of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='851 and accuracy of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='803 respectively in diagnosis of carotid atherosclerosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The automatically measured stenosis grade illustrated good correlation (r=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='762) with the experienced expert measurement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Conclusion: the developed technique based on 3D US imaging can be applied to the automatic diagnosis of carotid atherosclerosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Significance: The proposed deep-learning based technique was specially designed for a portable 3D freehand US system, which can provide carotid atherosclerosis examination more conveniently and decrease the dependence on clinician’s experience.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Index Terms—3D ultrasound imaging, automatic carotid atherosclerosis diagnosis, carotid artery segmentation, reconstruction with regularization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' INTRODUCTION AROTID atherosclerosis is one of the major causes of stroke which is the world’s second leading cause of death [1].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The prevalence rate of carotid atherosclerosis is 36.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2% in Chinese people over 40 years old [2].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The pathological features of carotid atherosclerosis are increase of intima-media thickness and appearance of atherosclerosis plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Magnetic resonance imaging (MRI), computed tomography angiography This work was sponsored by Natural Science Foundation of China (NSFC) under Grant No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='12074258.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (Jiawen Li and Yunqian Huang are co-first authors.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=') (Corresponding authors: Rui Zheng, Man Chen.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=') Jiawen Li, Sheng Song, Duo Xu and Haibin Zhang are with School of Information Science and Technology, ShanghaiTech University, Shanghai, China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Hongbo Chen is with School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China, also with Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200050, (CTA) and digital subtraction angiography (DSA) are several commonly used methods for visualizing and characterizing carotid artery features [3]–[5].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, these methods still have some limitations during application due to invasiveness, ionizing radiation, heavy equipment etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' and the approaches are very time-consuming and expensive which can’t satisfy the need of large scale of examinations in different environments especially for community and countryside areas.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 2D Ultrasound (US), as a non-invasive and low-cost method, is widely used in the examination of carotid plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, there are several disadvantages of traditional 2D US in the current ultrasound examination of carotid atherosclerosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (1) It is mainly carried out by experienced sonographers in hospital, and becomes a huge burden for health care system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (2) Routine health check is difficult for carotid atherosclerosis patients especially in rural or undeveloped area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (3) Routine ultrasound examination is a tedious, laborious, experience-dependent work for sonographers.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (4) Clinically, some metrics such as intima- media thickness (IMT), plaque thickness, plaque area, usually assess the severity of the carotid atherosclerosis in 2D US images, which is prone to variability and lack of 3D morphology of carotid plaque [6], [7].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3D US carotid artery imaging approaches mainly include mechanical scanning and tracked freehand scanning using various sensors e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=', magnetic tracked senor, optical tracked sensor, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=', [8] which can provide plaque volume estimation, 3D morphology of plaque and other 3D metrics for carotid atherosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The 3D techniques are found to be more accurate to evaluate the progress of carotid atherosclerosis [9]–[12].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, it is of great importance to develop a portable, reliable and cost- effective automatic ultrasound diagnostic technique for carotid atherosclerosis screening.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The automatic diagnosis of carotid atherosclerosis focuses on finding the biomarkers on the ultrasound images, for example China, and also with University of Chinese Academy of Sciences, Beijing 100049, China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Yunqian Huang and Junni Shi are with Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Man Chen is with Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China (e-mail: maggiech1221@126.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='com) Dr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Rui Zheng is with School of Information Science and Technology, Shanghai Engineering Research Center of Energy Efficient and Custom AI IC, ShanghaiTech University, Shanghai, China (phone: 86 21-2068 4452, e-mail: zhengrui@shanghaitech.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='edu.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='cn) Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3D Ultrasound Imaging System Jiawen Li, Yunqian Huang, Sheng Song, Hongbo Chen, Junni Shi, Duo Xu, Haibin Zhang, Man Chen*, Rui Zheng* C 2 vessel wall area, vessel wall volume or total plaque volume [13]–[15].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' These biomarkers are all bounded by the two boundaries of vessels, the media-adventitia boundary (MAB) and the lumen-intima boundary (LIB), thus identifying these two boundaries is an important issue during the carotid atherosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In recent years, deep learning methods has achieved excellent performance in medical image processing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Jiang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [16]–[18]designed a novel adaptive triple loss for carotid artery segmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' To utilize 3D information in 3D volume of carotid artery, Jiang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [19] introduced a fusion module to the U-Net segmentation network and yielded promising performance on carotid segmentation task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Zhou et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [20] proposed a deep learning-based MAB and LIB segmentation method, and a dynamic convolutional neural network (CNN) were applied to image patches in every slice of the 3D US images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' LIB segmentation was performed by U-Net based on the masks of the MAB since the LIB is inside the MAB.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The method achieved high accuracy but initial anchor points were still manually placed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Ruijter et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [21] created a generalized method to segment LIB using CNN.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Several U- Nets were compared and the experiments showed that the combination of various vessels such as radial, ulnar artery, or cephalic vein improved the segmentation performance of carotid artery.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' After segmentation, a 3D-geometry can be obtained for further therapy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Van Knippenberg et al [22] proposed an unsupervised learning method to solve the lack of data in carotid segmentation task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Azzopardi et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [23] designed a novel geometrically constrained loss functions and received improved segmentation results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Zhou et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [24] proposed a voxel based 3D segmentation neural network to segment the MAB and LIB in 3D volume directly.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Although the proposed algorithm achieved high accuracy with fast process, user’s interaction is yet required to identify ROI in the first and last slice of the volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' After region of interest (ROI) i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=', carotid artery is identified, further analysis needs to be performed to get significant clinical information for carotid atherosclerosis diagnosis such as the existence of plaque, carotid stenosis grade, type of the plaque, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Zhou et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [25],[26] applied 8 different backbone and UNet++ segmentation algorithm trained on 2D longitudinal US images to segment the plaque region and calculate the total plaque area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Xia et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [27] employed a CNN to categorize segmented carotid images into normal cases, thickening vessel wall cases and plaque cases.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Ma et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [28] proposed a multilevel strip pooling-based convolutional neural network to investigate the echogenicity of plaque which was found to be closely correlated with the risk of stroke.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Shen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [29] proposed a multi task learning method, the authors combined ultrasound reports and plaque type label to train a CNN to classify four different plaque type.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [30] utilized a novel vessel wall thickness mapping algorithm to evaluate the therapeutical performance on carotid atherosclerosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Zhou et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [31] utilized the unsupervised pretrained parameters of U-Net to train a plaque segmentation network with a small 3D carotid artery ultrasound dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Saba et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [32] used a deep learning based method to measure the carotid stenosis, three deep learning based systems were evaluated on 407 US dataset, and achieved AUC of 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='90, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='94 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='86 on the longitudinal US images respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Biswas et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' [33] proposed a two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in longitudinal US images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results showed that the proposed method achieved the lowest error compared to previous method.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The current 3D carotid imaging device was mainly based on mechanical system and hard to transport which was almost impossible to apply in community or rural area, therefore the portable freehand 3D ultrasound imaging system was required which can be easily applied for various scenarios.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, for the freehand 3D ultrasound reconstruction, the requested small voxel size and various noise would lead to reconstruction artifacts[34], [35].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' On the other hand, the clinicians in different scenarios were usually inexperienced so that the diagnosis results might be inaccurate and hard to reproduce compared with sonographers in clinical ultrasound department.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In this paper, we developed a new detection and classification technique based on deep-learning algorithms for carotid atherosclerosis diagnosis which can be employed to a portable freehand 3D US imaging system for fast screening.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Compared to other 3D ultrasound carotid artery imaging methods mainly focusing on carotid vessel wall segmentation [18], [20], [21], [24], the proposed method aimed at exploring an automatic and experience-independent technique and framework for fast carotid arteriosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The main contributions are outlined as follows.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Firstly, a portable freehand 3D US carotid imaging and diagnosis framework including deep-learning based segmentation, 3D reconstruction and automatic volume analysis was developed for fast carotid atherosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Secondly, a novel position regularization algorithm was designed to reduce the reconstruction error caused by freehand scan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Lastly, post analysis including automatic reprojection and stenosis measurement from 3D volume data provided visible qualitative results and quantitative results for atherosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' METHODS Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 1 showed the overview of data processing procedure including transverse image segmentation, 3D volume reconstruction, detection of carotid atherosclerosis and 3D carotid volume analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' MAB and LIB Segmentation Three consecutive frames were concatenated in channel dimension which is proved to be useful to improve the segmentation accuracy [36].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Since the adjacent frames contained lots of redundant information, the pseudo labels were generated using pseudo- labeling method to reduce the work load [37].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' One of every 5 neighbor frames were selected to be manually labeled by experienced sonographers and the other four frames were inferred by the network which was trained using the labeled frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' All generated pseudo labels were checked visually, the labels would be corrected if the segmentation is incorrect.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The intensity of the image was normalized to [0,1] as follows: 𝐼 = 𝐼 − 𝐼𝑚𝑖𝑛 𝐼𝑚𝑎𝑥 − 𝐼𝑚𝑖𝑛 (1) 3 where I represented the intensity of the image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Imax and Imin represent the max and minimum value of the intensity in the US image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' All images and corresponding labels were resized to 224*224 for segmentation network training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' U-Net was employed to segment the MAB and LIB in the transverse US image sequence [38].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The architecture of the network was illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The segmentation module consisted of two symmetrical sub-module which were encoder and decoder.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The number of channels for each convolutional layer were set to (64, 128, 256, 512, 512).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Each convolutional layer was followed by a batch normalization module and a rectification linear unit (ReLU) module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The two modules were connected using skip connection to exploit all resolution features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The loss function of the segmentation module was the combination of DSC loss and cross-entropy loss: 𝐿𝑜𝑠𝑠 = 𝐿𝑜𝑠𝑠𝑑𝑖𝑐𝑒 + 𝐿𝑜𝑠𝑠𝑐𝑒 (2) B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3D Reconstruction with Regularization After the MAB and LIB were identified in every slice of US image sequence, the 3D carotid artery volume was reconstructed using the Fast Dot Projection (FDP) method [39].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, some disturbances caused by the low precision of the magnetic sensor, inevitable hand shaking and breathing movement during carotid swept, would lead to the reconstruction errors and artifacts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The major problem was the repeated acquisition at the same or very close positions, and it caused large uncertainty at volume voxels and discontinuity in the reconstructed volume [40].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' To improve the image quality and decrease the uncertainty of 3D reconstructed volume, a total variation regularization [41] method was integrated with FDP reconstruction algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (1) For all the position information obtained from 3DUS device, it could be formulated as a set of rotation matrix 𝑅 and a translation 𝑡.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The tuple (𝑹, 𝒕) consisting of all 𝑅 and 𝑡 formed the special Euclidean group 𝑆𝐸(3) which was the semi-direct product of the rotation group 𝑆𝑂(3) and the translation group.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, the 𝑆𝐸(3) can be formulated as: 𝑆𝐸(3) = {(𝑅 𝑡 0 1) : 𝑅 ∈ 𝑆𝑂(3), 𝑡 ∈ ℝ3} (3) Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The architecture of the segmentation module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The pipeline of the proposed system and corresponding algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The top row demonstrated the process of the data acquisition, extraction of ROI and 3D reconstruction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The bottom row represented the process of 3D carotid volume analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The original image sequence and corresponding position information were firstly obtained by the 3D US device.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' U-Net segmentation algorithm and regularized Fast-Dot Projection algorithm was applied to extract the ROI and 3D carotid volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Then 3D carotid volume analysis included automatic stenosis grade measurement, longitudinal image reprojection and healthy/diseased cases classification was conducted based on the reconstructed volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=':Conv+Batchnorm+ReLU × 2 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=':Maxpooling ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=': Upsampling ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=':Concatenate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=':1×1 ConvSegmented masks ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Data collection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Pre-processed images ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='of LIB and MAB ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Image ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Segmentation ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Fast-Dot Projection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='3D ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Sequence ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Reconstruction ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Reconstruction ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Algorithm ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Total ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Position ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Variation ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Information ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Fast-Dot Projection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Regularization ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='3D ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Reconstruction ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Algorithm ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Reconstruction ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Stenosis Grade ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Stenosis rate ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='30° ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Measurement ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Projection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Reprojection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='0° ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Module ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Projection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Exist Plaque ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Diagnosis ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='or Not ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='+30° ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='Projection ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='4 ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='(2) The position signal obtained by the 3DUS system could ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='be considered as a set of entries which forms a vector 𝑷 = ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='(𝒑𝟏,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 𝒑𝟐,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' … ,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 𝒑𝒌) ∈ 𝑴𝒌,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' where 𝑘 was the number of entries and 𝑘 ∈ 𝑁,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 𝑀𝑘 was a manifold and 𝑀 = 𝑆𝐸(3).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Another signal set X were considered to be found when the following formula is minimal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' E(𝐱) = D(𝐱, 𝐩) + αR(𝐱), α > 0 (4) Where 𝐷(𝑥, 𝑝) was the penalizing term to reduce the variation between original signal P and resulted signal X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 𝑅(𝒙) was a regularized term to penalize the position saltation in the signal X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (3) The deviation penalized term D(x,p) could be defined as: 𝐷(𝐱, 𝐟) = ∑ 𝑘 𝑖=1 (ℎ ∘ 𝑑)(𝐱𝑖, 𝐩𝑖) (5) Where d(xi,pi) was the length of the geodesic which was defined as a shortest path on 𝑀 between two pose p and q [41].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' ℎ was defined as following: ℎ(𝑠) = {𝑠2, 𝑠 < 1/√2 √2𝑠 − 1/2, otherwise (6) Which was the Huber-Norm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (4) For the regularized term, it could be defined as the following: 𝑅(𝐱) = ∑ 𝑘−1 𝑖=1 (ℎ ∘ 𝑑)(𝐱𝑖, 𝐱𝑖+1) (7) Where d(xi,xi+1) could be considered as the first-order forward difference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The optimize problem in (4) could be solved using a cyclic proximal point algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, the original regularized algorithm couldn’t handle the scanning positions with large backward movements.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In this case, the position array was not sequential according to the coordinates, therefore pose re-rank algorithm was proposed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Concretely, considering the centroid point of every frame from the 2D segmented image sequence as 𝑪𝒌 = (𝒄𝟏, 𝒄𝟐, … , 𝒄𝒌) , the PCA (principal components analysis) algorithm was conducted in 𝐶𝑘 and a new matrix 𝐷𝑘 was obtained.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The first column of the matrix was the principal vector 𝑣𝑘, then a set of vectors 𝑐𝑑 could be acquired by projecting every centroid vector 𝑐𝑘 to 𝑣𝑘.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 𝒄𝒅 = 𝒄𝑘 − 𝒄𝒌 ⋅ 𝑣𝑘 𝑣𝑘 ⋅ 𝑣𝑘 𝑣𝑘 (8) The new position sequence was finally obtained by sorting the l2-norm of the 𝒄𝒅 set.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Carotid Atherosclerosis Diagnosis The US scans including the segmented and reconstructed volume were classified into healthy case and carotid atherosclerosis case using a diagnosis network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' As illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3, there were two inputs for the diagnosis module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' It had been proved that the morphological information was helpful for the network to classify the normal or abnormal (diseased) image [42], therefore the mask of LIB and MAB extracted from each slice of the reconstructed volume was used as one input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The other input was the cropped ROI which was determined by the max bounding rectangular of the mask, and in the cropped image, the intensity in the region between LIB and MAB was set to the original value, while the region inside lumen and outside vessel wall were set to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For each input stream, it consisted of three repeated blocks, each block consisted of two consequent basic convolutional sub-block and a max pooling layer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The basic convolutional sub-block was composed of a convolutional layer, a batch normalization module and a linear rectification unit.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The number of channels for each repeated block were set to (24, 48, 96).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The fusion block concatenated the high-level features of two streams and combined information by introducing a basic convolutional sub-block.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' After fusion block, the remaining layers were global average pooling (GAP) layers and a fully connected layer to output the diagnosis result.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' We used focal loss in the diagnosis module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The scan would be diagnosed as a carotid atherosclerosis case if the consecutive 5 transverse slices from the reconstructed volume were classified as existing plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3D Carotid Volume Analysis The clinical diagnostic parameters such as plaque thickness, plaque length, stenosis grade, plaque area, plaque type, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' can be directly calculated from the reconstructed carotid artery volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' To validate accuracy of the proposed method, the longitudinal US images of carotid artery were obtained by projecting the volume in different angles, and the stenosis grade was calculated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Stenosis rate was usually used to evaluate the stenosis grade.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For the slices which were diagnosed as atherosclerosis, stenosis degree can be evaluated using the LIB and MAB masks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The diameter stenosis rate was usually calculated to evaluate the stenosis grade in clinic.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' We denote it as 𝑆𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 = 𝐿𝑤𝑎𝑙𝑙 𝐿𝑤𝑎𝑙𝑙 + 𝐿𝑙𝑢𝑚𝑒𝑛 (9) where 𝐿 represented the length of respective area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The metric was ranged from 0 to 1, the greater number indicated the more severe stenosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The length of vessel wall 𝐿𝑤𝑎𝑙𝑙 and length of Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The architecture of the diagnosis module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 4 The illustration of the approach to calculate the diameter stenosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 24X128X128 128X128 48X64X64 96X32X32 96X16X16 96X16X16 Exist plaque 24X128X128 48X64X64 or not 96X32X32 :Conv+Batchnorm+ReLU 96X16X16 :Maxpooling 128X128 :Global average pooling :Concatenate + :Fully connectRadially Sampling :lumen :Vessel Wall 5 lumen 𝐿𝑙𝑢𝑚𝑒𝑛 were illustrated as Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The diameter stenosis rate was the max 𝑆𝑑𝑖𝑎𝑚𝑒𝑡𝑒𝑟 which was calculated using all points in MAB boundary.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The longitudinal carotid US images were usually used to calculate plaque size and evaluate the morphology of plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Since the carotid artery is curved volume, the direct projection along a fixed axis may lead to missing of some structure.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, centroid points of carotid artery in transverse slices were selected to determine the projection plane.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Specifically, as illustrated in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 5, denoting the centroid point of i-th slice in the volume as 𝐶𝑖, the line which was 𝜃 degree angled with y- axis through the centroid point 𝐶𝑖 , was sampled as the i-th column of projected longitude image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In our experiment, the longitudinal US images were obtained by reprojecting the 3D carotid volume at the angles of 0°, ±15° and ±30°.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' EXPERIMENTAL SETUP A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Data Acquisition and 3D Ultrasound Scan A portable freehand 3D ultrasound imaging system was used to obtain three-dimensional images of carotid artery as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The system consisted of a 2D linear probe (Clarius, L738-K, Canada), an electromagnetic tracking system (Polhemus, G4 unit, U.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='A) and a host laptop computer (Intel i7-8700k CPU @ 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='70GHz, 32GB RAM) [43].' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The 2D transverse US images were acquired by the probe while the corresponding position and orientation information were captured by the magnetic sensor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The images and orientation were acquired with a frame rate of 24 Hz.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' During the acquisition, the subjects took the supine position and was scanned as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 6 (d), and the probe swept consistently along the long axis of common carotid artery from the proximal end to the distal end at the speed of approximate 10-15 seconds per scan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' To reduce the reconstruction artifacts, fallback along the swept direction and large movement normal to the swept direction should be avoided.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The inclusion criteria were based on visible plaques which was identified by expert.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The stenosis grade larger than 70% was excluded to the dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' A total of 127 3D carotid artery scans from 83 subjects with stenosis range from 0% to 70% were obtained from local hospital, and all subjects consented to participate in this experiment, which was approved by the local ethics committee.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The age of the subjects was ranged from 51 to 86 years old (Male: 38, Female: 45).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Each scan contained 122-250 2D transverse US images with resolution of 640*480.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 7596 2D images from 40 scans were manually delineated for MAB and LIB and labeled for healthy or diseased (with plaque) by experienced sonographers for further training of segmentation and classification network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' All Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The illustration of the reprojection process.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The centroid point was calculated by the segmented MAB mask for each slice in the volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Then the line segment crosses the centroid point was set to conduct the reprojection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The red and green line segment represent the different resample angle respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (a) (b) (c) (d) Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Ultrasound scan using the freehand imaging system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (a) a handheld US scanner (left), a host laptop computer (middle) and an iPhone SE2 (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (b) Tracking system including a hub (left) and a RF/USB module (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (c) The sensor (left) and the magnetic source (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' (d) Ultrasound scan using the freehand imaging system.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Get Centroid 0 Theindexofthecolumn n GetCentroid i-1 Reprojection longitude image, reprojection angle=0o i i+1 Get Centroid Theindexofthecolumn n The index of the slice 6 127 scans were labeled for healthy or diseased (with plaque) by the same raters examining 2D images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In addition, stenosis grade and plaque size of randomly selected 20 scans were manually measured by expert using clinical 2D US device for verification of the proposed system and algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Training Methods 25 scans (4694 2D images) were randomly chosen for CNN training and 15 scans (2362 2D images) for validation in order to build and verify the segmentation module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The original images were resized to 224*224.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' All training process were performed using Pytorch 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='1 and Python 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='7 on a NVIDIA RTX 4000 GPU.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The two networks were trained separately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For the segmentation module, the applied data augmentation strategies including gamma transformation, rotation, zoom, horizontal and vertical flip, and Adam optimizer were used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The network was trained for 100 epochs with learning rate and batch size set to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='005 and 8 respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For the diagnosis module, the cropped and resized 2D US image segmented with the mask and the corresponding vessel wall mask were used for network training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Gamma transformation and horizontal & vertical flip were applied for data augmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The diagnosis network was trained for 50 epochs using Adam optimizer with learning rate and batch size set to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='005 and 64 respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Diagnosis parameter measurement To verify the regularized reconstruction and longitudinal images reprojection algorithm, the longitudinal images from 20 clinical patients with and without regularization were compared with clinical images acquired by experienced sonographers visually, and the projection angles were set as 𝜃 = −30°, −15°, 0°, 15°, 30°.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The plaque length and thickness were manually measured on the 3D pseudo volume, the reconstructed 3D volume and the clinical images acquired by experienced sonographers respectively, where 3D pseudo volume was the volume which were stacked directly by the 2D US images sequence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The manual measurement of plaque length and thickness was conducted on the reprojected longitudinal images, among which the reprojection angle was chosen based on the carotid structural integrity and maximum stenosis grade.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For plaque size measurement in reprojected image of reconstructed 3D volume, the pixel size was 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2 × 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2𝑚𝑚2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For the pseudo 3D volume, the velocity of the swept was assumed constant, therefore the pixel size of reprojected image was determined by the distance of the swept which could be calculated by the magnetic sensor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The whole system in clinical metric measurement was also verified by comparing stenosis rate automatically measured by the system and manually measured by experienced sonographers using clinical US device on 20 random clinical atherosclerosis patients according to formula (9).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Evaluation Metrics and Statistic Analysis The dice similarity coefficient (DSC) and 95% hausdorff distance (HD95) were used to evaluate the performance of the carotid sequence segmentation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' DSC indicated the quantitative metric of the overlap region between the ground truth and prediction mask which was defined as follows: 𝐷𝑆𝐶 = 2(𝑃 ∩ 𝐿) 𝑃 ∪ 𝐿 (10) where P, L were the prediction mask and ground truth.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The hausdorff distance was defined as Eq (11), which indicated the largest point-wise matching discrepancy: 𝐻𝐷(𝐴, 𝐵) = 𝑚𝑎𝑥(ℎ𝑑(𝐴, 𝐵), ℎ𝑑(𝐵, 𝐴)) (11) where ℎ𝑑(𝐴, 𝐵) = 𝑚𝑎𝑥𝑎∈𝐴(𝑚𝑖𝑛𝑏∈𝐵||𝑎 − 𝑏||) (12) ℎ𝑑(𝐵, 𝐴) = 𝑚𝑎𝑥𝑏∈𝐵(𝑚𝑖𝑛𝑎∈𝐴||𝑏 − 𝑎||) (13) For the evaluation of the diagnosis module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The specificity, sensitivity and accuracy were calculated for both 2D US image and scans.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The mean absolute difference (MAD) and standard deviation (SD) between results from the pseudo/reconstructed 3D volumes and results from experienced sonographers were investigated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The metrics in verification of the system, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=', the stenosis grade, were compared between manual or automatic approach using the proposed technique and manual measurement using the clinical US device with the Pearson correlation analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' RESULTS A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Segmentation and Diagnosis Accuracy The comparison between nine typical segmented images from U-Net and experienced sonographers was illustrated as Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 7, and the images were selected from different scans at some specific locations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Table I showed the average DSC and HD95 between the ground truth and prediction results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' TABLE I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' THE RESULTS OF VESSEL SEGMENTATION Metrics category MAB Lumen DSC 95.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='00% 93.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='30% HD95(pixel) 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='34 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='65 Table II showed the contingency table of the validation set of 2362 2D transverse images, and the sensitivity, specificity and accuracy were 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='73, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='97 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='91 respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Table III showed the diagnostic results of carotid atherosclerosis for all Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Comparison of the auto segmentation from U-net (red) and manual segmentation from ground truth (green).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 7 scans, and the sensitivity, specificity and accuracy of carotid atherosclerosis detection was 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='71, 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='85 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='80 respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' TABLE II.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' THE RESULTS OF DETECTION TEST FOR 2D IMAGES Labels Predictions Positive (plaque) Negative Positive (plaque) 454 171 Negative 50 1687 TABLE III.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' THE DETECTION RESULTS OF CA FOR SCANS Labels Predictions Positive (plaque) Negative Positive (plaque) 25 10 Negative 10 57 B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Reconstruction and Reprojection Accuracy Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 8 illustrated three representative examples of the longitudinal images without regularization, with regularization clinical US images acquired by experienced sonographers, and the corresponding orientation information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results revealed that the regularized reconstructed volume was smoother with less image artifacts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 9 demonstrated an example with large fallback of trajectory, the results showed there were still artifacts if directly apply the regularized algorithm and the proposed re-rank algorithm could remove the reconstruction artifacts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 10 illustrated the 3D volumes reconstructed from the auto-segmentation and ground truth respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The volumes were rendered by 3D-slicer (www.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='slicer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='org).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results showed that the segmentation module achieved good agreement with human label.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Furthermore, the sunken of the lumen area indicated the existence of the plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 11 Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Illustration of the US longitudinal images and the corresponding orientation information from three carotid atherosclerosis patients (by rows).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The images in the first column were reconstructed without regularization algorithm while the images in the third column were reconstructed with regularization algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The second column demonstrated the smoother results of the proposed algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The fourth column represents the images acquired by sonographers using clinical US devices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The images in fifth column illustrate the corresponding original position information and the images in sixth column show the regularized position information.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Illustration of the proposed re-rank algorithm, the first row demonstrated the longitudinal image and corresponding position information without regularized algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The second row represented the images which applied regularized algorithm and the third row showed the images which used re-rank and regularized algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 8 demonstrated comparison among 5 projected images in different angles ( 𝜃 = −30°, −15°, 0°, 15°, 30° ), the image directly projected to sagittal plane and the manually acquired image by expert from the same atherosclerosis patient.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results showed that the projected images in different angles could reveal more structures of the carotid than the images only projected to sagittal plane.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' On the other hand, in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 11, the image in 15° projection angle was most consistent with the clinical image obtained by expert using clinical US device, which indicated that the reprojection of 3D volume could simulate the different scan angles operated by expert to locate the best observation view.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The plaque size (length and thickness) measured from the pseudo volume, reconstructed volume and images acquired by expert were compared in Table IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results showed good agreement between the automatic measurement from the reconstructed volume and the manual method, while the plaque size measured by pseudo volume showed large difference with the expert measurement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results indicated that the 3D reconstruction could reveal the true geometry and clinical metric of the carotid artery.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' TABLE IV.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' MAD MEASUREMENTS (N=20) BETWEEN CLINICAL US DEVICE AND THE PROPOSED TECHNIQUES Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The 3D volumes from the auto-segmentation (the first row) and ground truth (the second row).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The translucent outer wall represents the vessel wall area, the inside red 3D volume represents the lumen area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The sunken of the lumen area indicated the existence of the plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The resolution of reconstruction is set to 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2×0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2×0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2 𝑚𝑚3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Illustration of the projected images in different angles, from left top to right bottom were 5 projected images (𝜃 = −30°, −15°, 0°, 15°, 30° ), direct sagittal image and clinical image respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' It could be observed the sagittal image missed the part of vessel wall (in the red box) and the reprojected image with 𝜃 = 15 ° showed the most consistent structure of plague with clinical image (in the green boxes shows).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 9 Plaque length(mm) Plaque thickness(mm) Plaque length (relative error) Plaque thickness (relative error 3D Reconstructed volume 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='65±2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='36 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='842±0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='617 15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='4%± 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='6% 26.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='0%± 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2% Direct stacked Pesudo volume 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='54±7.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='23 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='976±0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='648 40.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='0%± 48.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='0% 29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='4%± 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='0% C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Stenosis Measurement Accuracy Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 12 demonstrated the linear correlation (r=0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='762) of the stenosis grade measured by the system and experienced sonographers using the clinical US device on 20 carotid atherosclerosis patients, which indicated the proposed technique had the strong consistency with expert manual approach in carotid atherosclerosis diagnosis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' DISCUSSION In this study, we proposed a portable freehand 3D US imaging technique for carotid artery diagnosis which could achieve real 3D geometry of carotid artery, and the method showed good agreements with manual measurement of stenosis rate and classification of diseased and healthy case.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The system was transportable and less dependent on operator’s experience, which make it possible for routine health check in different environments such as community or rural area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In addition, the 3D reconstructed geometry could provide visualized carotid artery structure for further atherosclerosis evaluation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Since the large position variation or fallback movement during scan would cause reconstruction artifacts, we designed a standard scan protocol for 3D carotid US data acquisition and analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The whole processing steps included automatic 3D US data acquisition, MAB and LIB segmentation, 3D reconstruction, automatic classification and measurement.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In practice, the intermediate results of each step could be reviewed and manually corrected by operator if necessary to ensure the accurate final results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The diagnosis result was based on two key points: one was the accurate segmentation of vessel area, and the other was the correct reconstruction volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The segmentation determined the region of interest (ROI) used for following analysis including automatic stenosis evaluation, plaque size measurement and 3D geometry visualization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The wrong mask might crop regions out of the carotid artery, mislead the diagnosis network and cause confusing diagnosis results.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, if the 3D volume was directly reconstructed from original 2D frames before segmentation, the reconstruction artifacts around MAB and LIB such as misplacement or severe blurring could lead to segmentation error of vessels, especially for some cases with large position variation as Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' showed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, we conducted segmentation on the original 2D US image sequence before 3D reconstruction to extract the vessel area firstly to reduce the influence of reconstruction artifacts.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' For the reconstruction process, the failure reconstruction caused by large position variation could result in severe image artifacts which totally deviated the structure of the carotid artery as shown in Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 14 For the freehand US scan, theoretically, the position information recorded by magnetic sensor would be consistent with US probe motion i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=', the position of every US Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 12.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' correlation of stenosis grade between the manual measurement by expert using the clinical US device and the automatic measurement from the proposed technique on 20 carotid atherosclerosis patients.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 14.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Severe reconstruction artifacts caused by the large position variation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The image in first row represented the reconstructed volume and the orientation information with regularized algorithm while the images in the second row represented the results without regularized algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The left image shows the transverse image of the locations in the reconstructed volume marked in red boxes in the right image, the large distortion could be observed in the image while the distortion was alleviated using the regularized algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Segmentation results on a transverse image collected from the 3D volume reconstructed by the original image sequence (left) and on an original transverse frame data (right).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' It could be observed that the severe artifacts on the left image led to wrong segmentation result.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='3 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='1 0 0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='1 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='3 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='5 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='6 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='7 Stenosis grade measured by expert using clinicla US device 10 image.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, the low precision of the sensor and inevitable hand jitter would lead to the noticeable measurement uncertainty of the position information along the scan direction and influence the reconstruction accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, we adopted a novel total variation regularization algorithm to smooth the track of the position information and decrease distortion and disconnection of the image volume.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The position of the freehand scan can be regarded as continuous and sequential array;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' therefore, the proposed regularization algorithm could reduce the uncertainty by constructing and minimize a regularized formulate in the manifold of Euclidean transformations.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Meanwhile, a re-rank strategy was designed to solve the unordered image sequence caused by fallback movement during scan.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In the future, the reconstruction accuracy could be further improved using the neural network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' After segmentation and reconstruction, the carotid artery volume could be obtained for further analysis such as healthy or diseased case diagnosis, plaque thickness, length area measurement, plaque type identification and stenosis measurement etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In the diagnosis module, the cropped and resized images instead of the whole US images were used as the input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Since the plaque was only located inside vessel wall area, removing useless information outside the vessel wall could accelerate network training and improve the detection accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' On the other hand, there may be low intensity area in the vessel region which could mislead the network and result in wrong classification since negative sample (no plaque) usually had low intensity in lumen area.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Therefore, the MAB and LIB mask were introduced to combine the morphological information with original image information to improve the detection accuracy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, the proposed approach just utilized the consecutive 2D reconstructed transverse US images to detect plaque cases, thus some cases with small plaque size or severe artifacts were wrongly classified as no plaque.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In the future, we will take the z axis information into account and use the whole 3D volume as input instead of detecting plaque by limited consecutive transverse slices to improve the accuracy of the diagnosis module.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' We utilized a reprojection algorithm to project the carotid artery volume to longitudinal planes, so that the clinical metric such plaque length, thickness could be directly measured from the 3D volume with no need of new acquisition in sagittal direction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The traditional clinical carotid artery US examination required appropriate positioning and angle between transducer and neck,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' which greatly relies on the operator’s experience to localize the plaque and obtain a high-quality US image,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' the proposed reprojection approach in our method was not only relatively convenient but could reveal the complete structure of the carotid artery with only one scan,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' and the images obtained by our automatic method achieved great agreement with the images obtained by expert using clinical US device.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In segmentation module, we used U-Net to segment the MAB and LIB in 2D US image sequence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' Every image in the sequence was treated as a single image for the segmentation network.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' However, this approach didn’t exploit the context information in the adjacent frames.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In addition, some cases with severe noise or shadowing would result in wrong segmentation as Fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' 15 showed.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' In the future, 3D convolution will be considered to correct the segmentation mistake by utilizing the context information of the adjacent frames, and sample size will be enlarged to improve the accuracy and robustness of the segmentation algorithm.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' More 3D metrics such total plaque volume, vessel wall volume, etc.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' would be evaluated for more accurate validation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' On the other hand, the learning-based 3D reconstruction algorithm would be taken into account to improve the performance of reconstruction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' VI.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' CONCLUSION We have proposed an automatic 3D carotid artery imaging and diagnosis technique specially designed for the portable freehand ultrasound device.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The technique applied a novel 3D reconstructed algorithm and a robust segmentation algorithm for automatic carotid atherosclerosis analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' The results demonstrated that the technique achieved good agreement with manual expert examination on plaque diagnosis and stenosis grade measurement, which showed the potential application on fast carotid atherosclerosis examination and the follow-ups, especially for those scenarios where professional medical device and experienced clinicians are hard to acquire such as rural area or community with large population.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content=' ACKNOWLEDGEMENT This work was sponsored by Natural Science Foundation of China (NSFC) under Grant No.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/7tE1T4oBgHgl3EQfTwM_/content/2301.03081v1.pdf'} +page_content='12074258.' metadata={'source': 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