AnatomiCuts correspondences

This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm. Our implementation uses our anatomical similarity metric which allows us to find correspondences without the need of registration, comparing clusters of streamlines in each subject's native space.

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 two.

-h1 the path to the AnatomiCuts folder to be used for subject one.

-h2 the path to the AnatomiCuts folder to be used for subject two.

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

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

-o output csv file

Output

The output will be a csv file:

Subject one, Subject two
100000,11111
1010101, 100010

Where cluster 100000.trk from Subject one corresponds to cluster 11111.trk from Subject two

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.

AnatomiCuts_correspondences (last edited 2018-09-30 15:21:09 by VivianaSiless)