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