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The retinogeniculate pathway (RGVP) is responsible for carrying visualinformation from the retina to the lateral geniculate nucleus. Identificationand visualization of the RGVP are important in studying the anatomy of thevisual system and can inform treatment of related brain diseases. Diffusion MRI(dMRI) tractography is an advanced imaging method that uniquely enables in vivomapping of the 3D trajectory of the RGVP. Currently, identification of the RGVPfrom tractography data relies on expert (manual) selection of tractographystreamlines, which is time-consuming, has high clinical and expert labor costs,and affected by inter-observer variability. In this paper, we present what webelieve is the first deep learning framework, namely DeepRGVP, to enable fastand accurate identification of the RGVP from dMRI tractography data. We designa novel microstructure-informed supervised contrastive learning method thatleverages both streamline label and tissue microstructure information todetermine positive and negative pairs. We propose a simple and successfulstreamline-level data augmentation method to address highly imbalanced trainingdata, where the number of RGVP streamlines is much lower than that of non-RGVPstreamlines. We perform comparisons with several state-of-the-art deep learningmethods that were designed for tractography parcellation, and we show superiorRGVP identification results using DeepRGVP.

DeepRGVP: A Novel Microstructure-Informed Supervised Contrastive  Learning Framework for Automated Identification Of The Retinogeniculate  Pathway Using dMRI Tractography