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Abstract #4018

Automatic Tractography Segmentation by Morphological Continuity Clustering

Fang-Cheng Yeh1, Wen-Yih Isaac Tseng2

1Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States; 2Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, Taipei, Taiwan

We present a tractography segmentation algorithm called morphological continuity clustering (MCC), which is a fully automatic, unguided method that clusters fiber tracts without predefining the cluster number. This algorithm is based on the concept that the fibers of the same cluster share the morphological continuity, a feature used to determine whether two tracts should be grouped. The performance was evaluated on tractography with a total of 100,000 fibers tracts generated by streamline tracking method on generalized q-space imaging (GQI). The results showed that MCC is able to generate several clusters that correspond to well-known fiber tracts. Further study is needed to improve the accuracy and robustness of the proposed method.