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Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in\u00a0vivo 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\u2010consuming, has high clinical and expert labor costs, and is affected by inter\u2010observer variability. In this paper, we present a novel deep learning framework, DeepRGVP, to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure\u2010informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a new streamline\u2010level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non\u2010RGVP streamlines. In the experiments, we perform comparisons with several state\u2010of\u2010the\u2010art deep learning methods that were designed for tractography parcellation. Furthermore, to assess the generalizability of the proposed RGVP method, we apply our method to dMRI tractography data from neurosurgical patients with pituitary tumors. In comparison with the state\u2010of\u2010the\u2010art methods, we show superior RGVP identification results using DeepRGVP with significantly higher accuracy and F1 scores. In the patient data experiment, we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.<\/jats:p>","DOI":"10.1002\/hbm.70071","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T05:20:19Z","timestamp":1732080019000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Tractography\u2010Based Automated Identification of Retinogeniculate Visual Pathway With Novel Microstructure\u2010Informed Supervised Contrastive Learning"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4645-144X","authenticated-orcid":false,"given":"Sipei","family":"Li","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China  Chengdu China"},{"name":"Department of Bioengineering University of Pennsylvania  Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1727-4078","authenticated-orcid":false,"given":"Shun","family":"Yao","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital Sun Yat\u2010Sen University  Guangzhou China"},{"name":"Brigham and Women's Hospital Harvard Medical School  Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1797-8334","authenticated-orcid":false,"given":"Jianzhong","family":"He","sequence":"additional","affiliation":[{"name":"College of Information Engineering Zhejiang University of Technology  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tengfei","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science University of Sydney  New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery Nuclear Industry 215 Hospital of Shaanxi Province  Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqian","family":"Chen","sequence":"additional","affiliation":[{"name":"Brigham and Women's Hospital Harvard Medical School  Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erickson F.","family":"Torio","sequence":"additional","affiliation":[{"name":"Brigham and Women's Hospital Harvard Medical School  Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanjing","family":"Feng","sequence":"additional","affiliation":[{"name":"Brigham and Women's Hospital Harvard Medical School  Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dhiego C. 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