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chapters/collecting-trajectories.tex

@@ -234,22 +234,14 @@
         \end{column}
         \begin{column}{0.55\textwidth}
             \begin{itemize}[<+->]
-                \item A-B. Cell Tracking Challenge format
-				quality estimations in Table S3 are based on an ROI of a dataset (A), where each cell is manually
-				tracked for as long it appears in the ROI.
-                \item Manual track annotations are formatted in CTC format, to
-				which automated tracking with TrackMate is then compared to estimate tracking quality. Division metrics
-				in C-J are based on a dataset where each cell division is manually annotated (B).
-                \item  C-E, I.
-				Division detections for Oneat (not connected with tracks), TrackMate-Oneat (Oneat divisions connected
-				with TrackMate tracks), TrackMate-Oneat + MARI principle (TrackMate-Oneat with max boundary set
-				for angle between mother cell and daughter cells), and TrackMate “native” track splitting, enabled in
-				TrackMate LAP linking algorithm.
-                \item H, J. Corresponding detection metrics. K. Manually annotated
-				ground truth tracks colorized by the Track ID assigned by automatic tracking used for the experiments
-			 
+				\item \textbf{A-B.} ROI manual tracks (CTC format) → quality estimation
+				\item Ground truth: manual divisions; compare to automated tracks
+				\item \textbf{C-E, I.} Division detection: 
+					  Oneat / TrackMate-Oneat / +MARI / TrackMate-native
+				\item \textbf{H, J.} Detection metrics
+				\item \textbf{K.} Ground truth tracks colorized by auto Track IDs
+			\end{itemize}
 			
-            \end{itemize}
         \end{column}
     \end{columns}
 }

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presentation.toc

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 \beamer@subsectionintoc {1}{4}{Cell Fate Classification: XGBoost}{28}{0}{1}
 \beamer@sectionintoc {2}{Oneat: Action Classification}{34}{0}{2}
 \beamer@subsectionintoc {2}{1}{TrackMate-Oneat: Auto Track Correction}{36}{0}{2}
-\beamer@subsectionintoc {2}{2}{Lineage tree correction: TrackMate-Oneat}{40}{0}{2}
-\beamer@sectionintoc {3}{Tracking: Nuclei $\rightarrow $ Membrane: NapaTrackMater}{47}{0}{3}
-\beamer@subsectionintoc {3}{1}{Point Cloud based morphodynamic features: NapaTrackMater}{53}{0}{3}
-\beamer@subsectionintoc {3}{2}{Trajectory Analysis: NapaTrackMater}{59}{0}{3}
-\beamer@sectionintoc {4}{XGBoost: Feature Ablation}{64}{0}{4}
-\beamer@sectionintoc {5}{The Inception Model}{69}{0}{5}
-\beamer@subsectionintoc {5}{1}{Time Cross Correlations}{76}{0}{5}
-\beamer@subsectionintoc {5}{2}{Neural Network: Learning cross-correlations}{85}{0}{5}
+\beamer@subsectionintoc {2}{2}{Lineage tree correction: TrackMate-Oneat}{41}{0}{2}
+\beamer@sectionintoc {3}{Tracking: Nuclei $\rightarrow $ Membrane: NapaTrackMater}{48}{0}{3}
+\beamer@subsectionintoc {3}{1}{Point Cloud based morphodynamic features: NapaTrackMater}{54}{0}{3}
+\beamer@subsectionintoc {3}{2}{Trajectory Analysis: NapaTrackMater}{60}{0}{3}
+\beamer@sectionintoc {4}{XGBoost: Feature Ablation}{65}{0}{4}
+\beamer@sectionintoc {5}{The Inception Model}{70}{0}{5}
+\beamer@subsectionintoc {5}{1}{Time Cross Correlations}{77}{0}{5}
+\beamer@subsectionintoc {5}{2}{Neural Network: Learning cross-correlations}{86}{0}{5}