Differences between revisions 2 and 12 (spanning 10 versions)

AnatomiCuts correspondences

This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm. Importantly, by using our anatomical similarity metric, correspondences are found without the need of registration comparing clusters of streamlines from each subject's native space.

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Contents

AnatomiCuts correspondences

The Hungarian algorithm

The Hungarian algorithm finds corresponding clusters between two subjects.

AnatomiCuts_correspondences -s1 segmentation1.nii.gz -s2 segmentation2.nii.gz -c numClusters -h1 clusteringPath1  -h2 clusteringPath2 -m metric -o output.csv

Where

-s1 the segmentation to be used for anatomical similarity in subject one.

-s2 the segmentation to be used for anatomical similarity in subject one.

-h1 the path to the AnatomiCuts folder to be used for subject1.

-h2 the path to the AnatomiCuts folder to be used for subject2.

-m metric to be used: labels (anatomical similarity) or euclid (euclidean similarity).

-sym (under development) this flag will mirror the segmentation in subject 2 to find between hemisphere correspondences.

-o output csv file

Output

The output will be a csv file:

Subject A, Subject B
100000,11111
1010101, 100010

Where cluster 100000.trk from Subject A corresponds to cluster 11111.trk from Subject B

References

V. Siless, J. Y. Davidow, J. Nielsen, Q. Fan, T. Hedden, M. Hollinshead, C. V. Bustamante, M. K. Drews, K. R. A. Van Dijk, M.A. Sheridan, R. L. Buckner, B. Fischl, L. Somerville, and A. Yendiki. 2017. “Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan.”

V. Siless, K. Chang, B. Fischl, and A. Yendiki. 2018. “AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity.” NeuroImage, 166, Pp. 32-45.

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-This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm.
+This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm. Importantly, by using our anatomical similarity metric, correspondences are found without the need of registration comparing clusters of streamlines from each subject's native space.

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+== Output ==

The output will be a csv file:

{{{
Subject A, Subject B
100000,11111
1010101, 100010

}}}

Where cluster 100000.trk from Subject A corresponds to cluster 11111.trk from Subject B
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+V. Siless, J. Y. Davidow, J. Nielsen, Q. Fan, T. Hedden, M. Hollinshead, C. V. Bustamante, M. K. Drews, K. R. A. Van Dijk, M.A. Sheridan, R. L. Buckner, B. Fischl, L. Somerville, and A. Yendiki. 2017. “Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan.” 

V. Siless, K. Chang, B. Fischl, and A. Yendiki. 2018. “AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity.” NeuroImage, 166, Pp. 32-45.