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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. 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. | 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. |
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| -s2 the segmentation to be used for anatomical similarity in subject one. | -s2 the segmentation to be used for anatomical similarity in subject two. |
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| -h1 the path to the AnatomiCuts folder to be used for subject1. | -h1 the path to the AnatomiCuts folder to be used for subject one. |
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| -h2 the path to the AnatomiCuts folder to be used for subject2. | -h2 the path to the AnatomiCuts folder to be used for subject two. |
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| -sym (under development) this flag will mirror the segmentation in subject 2 to find between hemisphere correspondences. | -sym (under development) this flag will mirror the segmentation in subject two to find between hemisphere correspondences. |
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| Subject A, Subject B | Subject one, Subject two |
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| Where cluster 100000.trk from Subject A corresponds to cluster 11111.trk from Subject B | Where cluster 100000.trk from Subject one corresponds to cluster 11111.trk from Subject two |
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.
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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.