Abstract #1290

Exploring the possibilities with deep learning to compute shape measures of the brain's white matter connections

Yui Lo^1,2,3, Yuqian Chen^1,2, Dongnan Liu³, Jon Haitz Legarreta^1,2, Leo Zekelman^2,4, Jarrett Rushmore^5,6, Fan Zhang⁷, Yogesh Rathi^1,2, Nikos Makris^1,5, Alexandra J. Golby^1,2, Weidong Cai³, and Lauren J. O'Donnell^1,2

¹Harvard Medical School, Boston, MA, United States, ²Brigham and Women’s Hospital, Boston, MA, United States, ³The University of Sydney, Sydney, Australia, ⁴Harvard University, Cambridge, MA, United States, ⁵Massachusetts General Hospital, Boston, MA, United States, ⁶Boston University, Boston, MA, United States, ⁷University of Electronic Science and Technology of China, Chengdu, China

Synopsis

Keywords: Analysis/Processing, Tractography, Shape

Motivation: Studies have shown the potential of tractography shape measures to provide insight into the brain’s structural connections and their relationship to human cognition. However, existing shape computation methods can be highly time-consuming when dealing with large-scale tractography datasets.

Goal(s): We investigate the possibility of deep learning to compute shape measures of the brain's white matter connections.

Approach: We propose a novel framework that leverages a point cloud representation of tractography to compute shape measures.

Results: TractShapeNet outperforms other point cloud-based models for shape computation. Results demonstrate that our approach enables faster and more efficient shape-measure computation than the conventional DSI-Studio.

Impact: We investigate the possibility of deep learning to compute shape measures of the brain's white matter connections without intermediate steps to convert geometric tractography streamline data to an image data representation using a voxel grid with our novel framework, TractShapeNet.

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