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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. |
AnatomiCuts correspondences
This method finds corresponding clusters across subjects. Currently, only one-to-one cluster's correspondences are available using the Hungarian algorithm.
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
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.