Abstract #1165

A novel deep learning method for automated identification of the retinogeniculate pathway using dMRI tractography

Sipei Li^1,2, Jianzhong He^2,3, Tengfei Xue^2,4, Guoqiang Xie^2,5, Shun Yao^2,6, Yuqian Chen^2,4, Erickson F. Torio², Yuanjing Feng³, Dhiego CA Bastos², Yogesh Rathi², Nikos Makris^2,7, Ron Kikinis², Wenya Linda Bi², Alexandra J Golby², Lauren J O’Donnell², and Fan Zhang²

¹University of Electronic Science and Technology of China, Chengdu, China, ²Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, ³Zhejiang University of Technology, Hangzhou, China, ⁴University of Sydney, Sydney, Australia, ⁵Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China, ⁶The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China, ⁷Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Nerves, BrainWe present a novel deep learning framework, DeepRGVP, for the retinogeniculate pathway (RGVP) identification from dMRI tractography data. We propose a novel microstructure-supervised contrastive learning method (MicroSCL) that leverages both streamline labels and tissue microstructure (fractional anisotropy) for RGVP and non-RGVP. We propose a simple and effective streamline-level data augmentation method (StreamDA) to address highly imbalanced training data. We perform comparisons with three state-of-the-art methods on an RGVP dataset. Experimental results show that DeepRGVP has superior RGVP identification performance.

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