Tractography-embedded white matter stream clustering

• Published in: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) pp. 432 - 435
• Main Authors: Yan Jin, Cetingul, H. Ertan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2015
• Subjects: Accuracy; Clustering algorithms; Clustering methods; diffusion magnetic resonance imaging; drift detection; fiber tracking; Magnetic resonance imaging; Monitoring; Phantoms
• ISSN: 1945-7928; 1945-8452
• DOI: 10.1109/ISBI.2015.7163904

While automated segmentation of white matter fibers is essential for understanding the human brain connectome, fast unsupervised clustering of these fibers emanating from a manually specified region of interest (ROI) into tracts is more desired in a clinical environment. In this work, we propose a tractography-embedded white matter stream clustering method to apply fiber tracking and clustering in a simultaneous manner. Integrated into a filtered tractography scheme, our method continuously checks for a drift in the fiber trajectories, which in turn controls the timing of the clustering. This affinity propagation-based clustering only involves a small portion of fibers and exemplars are selected to label the rest of the fibers. The proposed method is found to be five times faster than a traditional clustering framework, yet still achieves high accuracy on phantom and real data.