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

We 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.