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Medical_Segmentation_Decathlon

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The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 299, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
                  raise ValueError(
              ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 353, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 304, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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🏆 Medical Segmentation Decathlon Dataset

📝 Overview

The Medical Segmentation Decathlon (MSD) is a comprehensive benchmark dataset for validating algorithms in 3D medical image segmentation. It includes 10 distinct tasks, each with unique challenges like small data sizes, unbalanced labels, varying object scales, multi-class labels, and multimodal imaging.

🔗 Dataset Access

🧩 Task Descriptions and Download Links

🧠 Task 01: Brain Tumours

  • Target: Gliomas segmentation (necrotic/active tumour and oedema)
  • Modality: Multimodal MRI (FLAIR, T1w, T1gd, T2w)
  • Size: 750 4D volumes (484 Training + 266 Testing)
  • Source: BRATS 2016 & 2017 datasets
  • Challenge: Complex, heterogeneously-located targets
  • 📥 Download: Task01_BrainTumour.tar

❤️ Task 02: Heart

  • Target: Left Atrium
  • Modality: Mono-modal MRI
  • Size: 30 3D volumes (20 Training + 10 Testing)
  • Source: King’s College London
  • Challenge: Small dataset with high variability
  • 📥 Download: Task02_Heart.tar

🫁 Task 03: Liver

  • Target: Liver and tumour
  • Modality: Portal venous phase CT
  • Size: 201 3D volumes (131 Training + 70 Testing)
  • Source: IRCAD Hôpitaux Universitaires
  • Challenge: Unbalanced labels with large (liver) and small (tumour) targets
  • 📥 Download: Task03_Liver.tar

🧬 Task 04: Hippocampus

  • Target: Hippocampus head and body
  • Modality: Mono-modal MRI
  • Size: 394 3D volumes (263 Training + 131 Testing)
  • Source: Vanderbilt University Medical Center
  • Challenge: High-precision segmentation of small neighboring structures
  • 📥 Download: Task04_Hippocampus.tar

🧑‍⚕️ Task 05: Prostate

  • Target: Prostate central gland and peripheral zone
  • Modality: Multimodal MRI (T2, ADC)
  • Size: 48 4D volumes (32 Training + 16 Testing)
  • Source: Radboud University Medical Centre
  • Challenge: Segmenting two adjacent regions with large variations
  • 📥 Download: Task05_Prostate.tar

🌬️ Task 06: Lung

  • Target: Lung and tumours
  • Modality: CT
  • Size: 96 3D volumes (64 Training + 32 Testing)
  • Source: The Cancer Imaging Archive
  • Challenge: Small target (cancer) in a large image
  • 📥 Download: Task06_Lung.tar

🍃 Task 07: Pancreas

  • Target: Pancreas and tumour
  • Modality: Portal venous phase CT
  • Size: 420 3D volumes (282 Training + 139 Testing)
  • Source: Memorial Sloan Kettering Cancer Center
  • Challenge: Unbalanced labels with large, medium, and small structures
  • 📥 Download: Task07_Pancreas.tar

🩸 Task 08: Hepatic Vessels

  • Target: Hepatic vessels and tumour
  • Modality: CT
  • Size: 443 3D volumes (303 Training + 140 Testing)
  • Source: Memorial Sloan Kettering Cancer Center
  • Challenge: Small, tubular structures near a heterogeneous tumour
  • 📥 Download: Task08_HepaticVessel.tar

🌿 Task 09: Spleen

  • Target: Spleen
  • Modality: CT
  • Size: 61 3D volumes (41 Training + 20 Testing)
  • Source: Memorial Sloan Kettering Cancer Center
  • Challenge: Large foreground size variation
  • 📥 Download: Task09_Spleen.tar

🩹 Task 10: Colon

  • Target: Colon Cancer Primaries
  • Modality: CT
  • Size: 190 3D volumes (126 Training + 64 Testing)
  • Source: Memorial Sloan Kettering Cancer Center
  • Challenge: Heterogeneous appearance
  • 📥 Download: Task10_Colon.tar

📜 License

The data is available under a permissive CC-BY-SA 4.0 license, allowing sharing, distribution, and further development.

🖋️ Citation

Please cite the following paper when using this dataset:

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

📏 Assessment Metrics

Performance is evaluated using:

  • 🥇 Dice Score (DSC)
  • 📐 Normalized Surface Distance (NSD)

📬 Contact

For questions, please reach out to the organizers at: [email protected]

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