{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:09:21Z","timestamp":1768507761623,"version":"3.49.0"},"posted":{"date-parts":[[2024,1,4]]},"group-title":"Neuroscience","reference-count":75,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2024,1,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  The retinogeniculate visual 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 the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables\n                  <jats:italic>in vivo<\/jats:italic>\n                  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 is affected by inter-observer variability. In this paper, we present a novel deep learning framework,\n                  <jats:italic>DeepRGVP<\/jats:italic>\n                  , 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. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and 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.\n                <\/jats:p>","DOI":"10.1101\/2024.01.03.574115","type":"posted-content","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T11:15:47Z","timestamp":1704453347000},"source":"Crossref","is-referenced-by-count":1,"title":["Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning"],"prefix":"10.64898","author":[{"given":"Sipei","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shun","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianzhong","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ce","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingjing","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"Xue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuqian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erickson F.","family":"Torio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanjing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhiego CA","family":"Bastos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yogesh","family":"Rathi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikos","family":"Makris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ron","family":"Kikinis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenya Linda","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexandra J","family":"Golby","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lauren J","family":"O\u2019Donnell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5032-6039","authenticated-orcid":false,"given":"Fan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"issue":"1","key":"2024010921480364000_2024.01.03.574115v1.1","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1007\/s10792-016-0229-0","article-title":"Correlation of the measurements of optical coherence tomography and diffuse tension imaging of optic pathways in amblyopia","volume":"37","year":"2017","journal-title":"International Ophthalmology"},{"key":"2024010921480364000_2024.01.03.574115v1.2","doi-asserted-by":"crossref","unstructured":"Astolfi, P. , Verhagen, R. , Petit, L. , Olivetti, E. , Masci, J. , Boscaini, D. , & Avesani, P . (2020). Tractogram Filtering of Anatomically Non-plausible Fibers with Geometric Deep Learning. Medical Image Computing and Computer-Assisted Intervention (MICCAI ), 291\u2013301.","DOI":"10.1007\/978-3-030-59728-3_29"},{"key":"2024010921480364000_2024.01.03.574115v1.3","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24411"},{"issue":"3","key":"2024010921480364000_2024.01.03.574115v1.4","doi-asserted-by":"crossref","first-page":"737","DOI":"10.1002\/jmri.25941","article-title":"Track-weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy","volume":"48","year":"2018","journal-title":"J. Magn. Reson. Imaging"},{"key":"2024010921480364000_2024.01.03.574115v1.5","doi-asserted-by":"crossref","first-page":"106113","DOI":"10.1016\/j.cmpb.2021.106113","article-title":"Data augmentation using generative adversarial neural networks on brain structural connectivity in multiple sclerosis","volume":"206","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"2024010921480364000_2024.01.03.574115v1.6","doi-asserted-by":"publisher","DOI":"10.1002\/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O"},{"key":"2024010921480364000_2024.01.03.574115v1.7","doi-asserted-by":"publisher","DOI":"10.1001\/archopht.121.7.944"},{"key":"2024010921480364000_2024.01.03.574115v1.8","doi-asserted-by":"crossref","unstructured":"Benou, I. , & Raviv, T. R . (2019). DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography. Medical Image Computing and Computer-Assisted Intervention (MICCAI ), 626\u2013635.","DOI":"10.1007\/978-3-030-32248-9_70"},{"key":"2024010921480364000_2024.01.03.574115v1.9","doi-asserted-by":"crossref","first-page":"103494","DOI":"10.1016\/j.nicl.2023.103494","article-title":"Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods","volume":"39","year":"2023","journal-title":"Neuroimage Clin"},{"key":"2024010921480364000_2024.01.03.574115v1.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.08.073"},{"issue":"3","key":"2024010921480364000_2024.01.03.574115v1.11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1136\/jnnp.11.3.211","article-title":"The laminar pattern of the lateral geniculate body in the primates. Journal of Neurology","volume":"11","year":"1948","journal-title":"Neurosurgery & Psychiatry"},{"key":"2024010921480364000_2024.01.03.574115v1.12","doi-asserted-by":"crossref","unstructured":"Charles, R. Q. , Su, H. , Kaichun, M. , & Guibas, L. J . (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), 77\u201385.","DOI":"10.1109\/CVPR.2017.16"},{"key":"2024010921480364000_2024.01.03.574115v1.13","first-page":"1597","article-title":"A Simple Framework for Contrastive Learning of Visual Representations","volume":"119","year":"2020","journal-title":"International Conference on Machine Learning (ICML)"},{"key":"2024010921480364000_2024.01.03.574115v1.14","doi-asserted-by":"crossref","first-page":"120086","DOI":"10.1016\/j.neuroimage.2023.120086","article-title":"Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation","volume":"273","year":"2023","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.15","doi-asserted-by":"crossref","first-page":"e509","DOI":"10.1016\/j.wneu.2019.03.190","article-title":"Correlation of Preoperative Cranial Nerve Diffusion Tensor Tractography with Intraoperative Findings in Surgery of Cerebellopontine Angle Tumors","volume":"127","year":"2019","journal-title":"World Neurosurgery"},{"key":"2024010921480364000_2024.01.03.574115v1.16","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.nicl.2017.07.020","article-title":"A test-retest study on Parkinson\u2019s PPMI dataset yields statistically significant white matter fascicles","volume":"16","year":"2017","journal-title":"NeuroImage. Clinical"},{"issue":"3","key":"2024010921480364000_2024.01.03.574115v1.17","doi-asserted-by":"crossref","first-page":"527","DOI":"10.3171\/2014.5.JNS131819","article-title":"Descriptive epidemiology of pituitary tumors in the United States, 2004-2009","volume":"121","year":"2014","journal-title":"Journal of Neurosurgery"},{"key":"2024010921480364000_2024.01.03.574115v1.18","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.04.127"},{"key":"2024010921480364000_2024.01.03.574115v1.19","unstructured":"Gunel, B. , Du, J. , Conneau, A. , & Stoyanov, V. (2020). Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning. In arXiv [cs.CL]. arXiv. http:\/\/arxiv.org\/abs\/2011.01403"},{"issue":"12","key":"2024010921480364000_2024.01.03.574115v1.20","first-page":"4338","article-title":"Deep Learning for 3D Point Clouds: A Survey","volume":"43","year":"2020","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"2024010921480364000_2024.01.03.574115v1.21","doi-asserted-by":"crossref","unstructured":"Gupta, V. , Thomopoulos, S. I. , Rashid, F. M. , & Thompson, P. M . (2017). FiberNET: An Ensemble Deep Learning Framework for Clustering White Matter Fibers. Medical Image Computing and Computer-Assisted Intervention (MICCAI ), 548\u2013555.","DOI":"10.1007\/978-3-319-66182-7_63"},{"key":"2024010921480364000_2024.01.03.574115v1.22","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.nicl.2017.10.010","article-title":"Delineation of the visual pathway in paediatric optic pathway glioma patients using probabilistic tractography, and correlations with visual acuity","volume":"17","year":"2018","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.23","unstructured":"Han, T. , Huang, H. , Yang, Z. , & Han, W. (2021). Supervised Contrastive Learning for Accented Speech Recognition. In arXiv [cs.SD]. arXiv. http:\/\/arxiv.org\/abs\/2107.00921"},{"issue":"12","key":"2024010921480364000_2024.01.03.574115v1.24","doi-asserted-by":"crossref","first-page":"3887","DOI":"10.1002\/hbm.25472","article-title":"Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI","volume":"42","year":"2021","journal-title":"Human Brain Mapping"},{"key":"2024010921480364000_2024.01.03.574115v1.25","doi-asserted-by":"crossref","unstructured":"He, K. , Zhang, X. , Ren, S. , & Sun, J . (2015). Deep Residual Learning for Image Recognition. Computer Vision and Pattern Recognition (CVPR ), 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"2024010921480364000_2024.01.03.574115v1.26","first-page":"15","article-title":"Reconstruction and dissection of the entire human visual pathway using diffusion tensor MRI","volume":"4","year":"2010","journal-title":"Frontiers in Neuroanatomy"},{"issue":"1","key":"2024010921480364000_2024.01.03.574115v1.27","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3340\/jkns.2015.57.1.23","article-title":"The influence of pituitary adenoma size on vision and visual outcomes after trans-sphenoidal adenectomy: a report of 78 cases","volume":"57","year":"2015","journal-title":"Journal of Korean Neurosurgical Society"},{"key":"2024010921480364000_2024.01.03.574115v1.28","doi-asserted-by":"crossref","unstructured":"Huang, Q. , Ko, T. , Lilian Tang, H. , Liu, X. , & Wu, B. (2021). Token-Level Supervised Contrastive Learning for Punctuation Restoration. In arXiv [cs.CL]. arXiv. http:\/\/arxiv.org\/abs\/2107.09099","DOI":"10.21437\/Interspeech.2021-661"},{"issue":"1","key":"2024010921480364000_2024.01.03.574115v1.29","first-page":"1","article-title":"Survey on deep learning with class imbalance","volume":"6","year":"2019","journal-title":"Journal of Big Data"},{"key":"2024010921480364000_2024.01.03.574115v1.30","unstructured":"Khosla, P. , Teterwak, P. , Wang, C. , Sarna, A. , Tian, Y. , Isola, P. , Maschinot, A. , Liu, C. , & Krishnan, D . (2020). Supervised Contrastive Learning. Conference on Neural Information Processing Systems (NeurIPS ), 18661\u201318673."},{"key":"2024010921480364000_2024.01.03.574115v1.31","unstructured":"Kingma, D. P. , & Ba, J . (2014). Adam: A Method for Stochastic Optimization. arXiv, arXiv:1412.6980. arXiv."},{"key":"2024010921480364000_2024.01.03.574115v1.32","doi-asserted-by":"crossref","unstructured":"Kopuklu, O. , Zheng, J. , Xu, H. , & Rigoll, G . (2021). Driver anomaly detection: A dataset and contrastive learning approach. IEEE Winter Conference on Applications of Computer Vision (WACV ), 91\u2013100.","DOI":"10.1109\/WACV48630.2021.00014"},{"key":"2024010921480364000_2024.01.03.574115v1.33","first-page":"1057412","article-title":"TRAFIC: Fiber Tract Classification Using Deep Learning","volume":"10574","year":"2018","journal-title":"Proc SPIE Int Soc Opt Eng"},{"issue":"6","key":"2024010921480364000_2024.01.03.574115v1.34","doi-asserted-by":"crossref","first-page":"717","DOI":"10.3171\/jns.1977.46.6.0717","article-title":"Transsphenoidal decompression of the optic nerve and chiasm. Visual results in 62 patients","volume":"46","year":"1977","journal-title":"Journal of Neurosurgery"},{"key":"2024010921480364000_2024.01.03.574115v1.35","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y. , Goyal, P. , Girshick, R. , He, K. , & Doll\u00e1r, P . (2017). Focal Loss for Dense Object Detection. IEEE International Conference on Computer Vision, 2980\u20132988.","DOI":"10.1109\/ICCV.2017.324"},{"key":"2024010921480364000_2024.01.03.574115v1.36","doi-asserted-by":"crossref","unstructured":"Li, S. , He, J. , Xue, T. , Xie, G. , Yao, S. , Chen, Y. , Torio, E. F. , Feng, Y. , Bastos, D. C. A. , Rathi, Y. , Makris, N. , Kikinis, R. , Bi, W. L. , Golby, A. J. , O\u2019Donnell, L. J. , & Zhang, F . (2023). DEEPRGVP: A novel microstructure-informed supervised contrastive learning framework for automated identification of the retinogeniculate pathway using dMRI tractography. IEEE International Symposium on Biomedical Imaging (ISBI ), 1\u20135.","DOI":"10.1109\/ISBI53787.2023.10230833"},{"issue":"6","key":"2024010921480364000_2024.01.03.574115v1.37","first-page":"805","article-title":"Preoperative Visualization of Cranial Nerves in Skull Base Tumor Surgery Using Diffusion Tensor Imaging Technology","volume":"26","year":"2016","journal-title":"Turkish Neurosurgery"},{"key":"2024010921480364000_2024.01.03.574115v1.38","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0332"},{"key":"2024010921480364000_2024.01.03.574115v1.39","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2008.07.009"},{"key":"2024010921480364000_2024.01.03.574115v1.40","doi-asserted-by":"publisher","DOI":"10.1007\/s13311-018-00697-x"},{"key":"2024010921480364000_2024.01.03.574115v1.41","unstructured":"Paszke, A. , Gross, S. , Massa, F. , Lerer, A. , Bradbury, J. , Chanan, G. , Killeen, T. , Lin, Z. , Gimelshein, N. , Antiga, L. , Desmaison, A. , Kopf, A. , Yang, E. , DeVito, Z. , Raison, M. , Tejani, A. , Chilamkurthy, S. , Steiner, B. , Fang, L. , \u2026 Chintala, S . (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32."},{"issue":"12","key":"2024010921480364000_2024.01.03.574115v1.42","doi-asserted-by":"crossref","first-page":"4308","DOI":"10.1093\/brain\/awac030","article-title":"Anterior optic pathway pathology in CNS demyelinating diseases","volume":"145","year":"2022","journal-title":"Brain"},{"key":"2024010921480364000_2024.01.03.574115v1.43","doi-asserted-by":"crossref","first-page":"102055","DOI":"10.1016\/j.nicl.2019.102055","article-title":"Quantifying nerve decussation abnormalities in the optic chiasm","volume":"24","year":"2019","journal-title":"NeuroImage. Clinical"},{"key":"2024010921480364000_2024.01.03.574115v1.44","unstructured":"Qi, C. R. , Su, H. , Mo, K. , & Guibas, L. J . (2017). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), 652\u2013660."},{"key":"2024010921480364000_2024.01.03.574115v1.45","first-page":"166","article-title":"Joint Multi-Fiber NODDI Parameter Estimation and Tractography Using the Unscented Information Filter","volume":"10","year":"2016","journal-title":"Frontiers in Neuroscience"},{"issue":"4","key":"2024010921480364000_2024.01.03.574115v1.46","doi-asserted-by":"crossref","first-page":"697","DOI":"10.3171\/2011.12.JNS111404","article-title":"Preoperative identification of the facial nerve in patients with large cerebellopontine angle tumors using high-density diffusion tensor imaging","volume":"116","year":"2012","journal-title":"Journal of Neurosurgery"},{"issue":"2","key":"2024010921480364000_2024.01.03.574115v1.47","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1001\/archopht.1954.00920040163002","article-title":"Notching of the optic chiasm by overlying arteries in pituitary tumors","volume":"51","year":"1954","journal-title":"A.M.A. Archives of Ophthalmology"},{"issue":"6","key":"2024010921480364000_2024.01.03.574115v1.48","doi-asserted-by":"crossref","first-page":"951","DOI":"10.3171\/jns.2004.101.6.0951","article-title":"Surgical management of meningiomas involving the optic nerve sheath","volume":"101","year":"2004","journal-title":"Journal of Neurosurgery"},{"key":"2024010921480364000_2024.01.03.574115v1.49","doi-asserted-by":"crossref","unstructured":"Schiefer, U. , & Hart, W. (2007). Functional Anatomy of the Human Visual Pathway. In U. Schiefer , H. Wilhelm , & W. Hart (Eds.), Clinical Neuro-Ophthalmology: A Practical Guide. Springer Berlin Heidelberg.","DOI":"10.1007\/978-3-540-32708-0"},{"key":"2024010921480364000_2024.01.03.574115v1.50","doi-asserted-by":"crossref","unstructured":"Schiffer, C. , Amunts, K. , Harmeling, S. , & Dickscheid, T . (2021). Contrastive Representation Learning For Whole Brain Cytoarchitectonic Mapping In Histological Human Brain Sections. IEEE International Symposium on Biomedical Imaging (ISBI ), 603\u2013606.","DOI":"10.1109\/ISBI48211.2021.9433986"},{"issue":"1","key":"2024010921480364000_2024.01.03.574115v1.51","first-page":"1","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","year":"2019","journal-title":"Journal of Big Data"},{"key":"2024010921480364000_2024.01.03.574115v1.52","doi-asserted-by":"publisher","DOI":"10.3171\/2015.7.JNS142922"},{"issue":"2","key":"2024010921480364000_2024.01.03.574115v1.53","doi-asserted-by":"crossref","first-page":"e1004692","DOI":"10.1371\/journal.pcbi.1004692","article-title":"Ensemble Tractography","volume":"12","year":"2016","journal-title":"PLoS Computational Biology"},{"key":"2024010921480364000_2024.01.03.574115v1.54","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0101524"},{"key":"2024010921480364000_2024.01.03.574115v1.55","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"key":"2024010921480364000_2024.01.03.574115v1.56","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.neuroimage.2018.07.070","article-title":"TractSeg - Fast and accurate white matter tract segmentation","volume":"183","year":"2018","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.57","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101559"},{"key":"2024010921480364000_2024.01.03.574115v1.58","doi-asserted-by":"crossref","unstructured":"Wen, Y. , Zhang, K. , Li, Z. , & Qiao, Y . (2016). A Discriminative Feature Learning Approach for Deep Face Recognition. European Conference on Computer Vision (ECCV ), 499\u2013515.","DOI":"10.1007\/978-3-319-46478-7_31"},{"key":"2024010921480364000_2024.01.03.574115v1.59","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2003.11.001"},{"key":"2024010921480364000_2024.01.03.574115v1.60","doi-asserted-by":"crossref","first-page":"102160","DOI":"10.1016\/j.nicl.2019.102160","article-title":"Anatomical assessment of trigeminal nerve tractography using diffusion MRI: A comparison of acquisition b-values and single- and multi-fiber tracking strategies","volume":"25","year":"2020","journal-title":"NeuroImage: Clinical"},{"key":"2024010921480364000_2024.01.03.574115v1.61","doi-asserted-by":"crossref","first-page":"102759","DOI":"10.1016\/j.media.2023.102759","article-title":"Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions","volume":"85","year":"2023","journal-title":"Medical Image Analysis"},{"key":"2024010921480364000_2024.01.03.574115v1.62","doi-asserted-by":"crossref","first-page":"102759","DOI":"10.1016\/j.media.2023.102759","article-title":"Superficial white matter analysis: An efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions","volume":"85","year":"2023","journal-title":"Medical Image Analysis"},{"issue":"8","key":"2024010921480364000_2024.01.03.574115v1.63","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1109\/TMI.2019.2902073","article-title":"Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery using Diffusion Tractography and Convolutional Neural Networks","volume":"38","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2024010921480364000_2024.01.03.574115v1.64","doi-asserted-by":"crossref","unstructured":"Xu, H. , Dong, M. , Nakai, Y. , Asano, E. , & Jeong, J.-W . (2018). Automatic detection of eloquent axonal pathways in diffusion tractography using intracanial electrical stimulation mapping and convolutional neural networks. IEEE International Symposium on Biomedical Imaging (ISBI ), 1034\u20131037.","DOI":"10.1109\/ISBI.2018.8363748"},{"key":"2024010921480364000_2024.01.03.574115v1.65","unstructured":"Xu, K. , Ba, J. , Kiros, R. , Cho, K. , Courville, A. , Salakhutdinov, R. , Zemel, R. , & Bengio, Y . (2015). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. International Conference on Machine Learning, 2048\u20132057."},{"key":"2024010921480364000_2024.01.03.574115v1.66","doi-asserted-by":"publisher","DOI":"10.1227\/NEU.0000000000001241"},{"key":"2024010921480364000_2024.01.03.574115v1.67","doi-asserted-by":"publisher","DOI":"10.1109\/TCDS.2023.3304529"},{"key":"2024010921480364000_2024.01.03.574115v1.68","doi-asserted-by":"crossref","first-page":"118870","DOI":"10.1016\/j.neuroimage.2021.118870","article-title":"Quantitative mapping of the brain\u2019s structural connectivity using diffusion MRI tractography: A review","volume":"249","year":"2022","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.69","doi-asserted-by":"crossref","first-page":"101761","DOI":"10.1016\/j.media.2020.101761","article-title":"Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation","volume":"65","year":"2020","journal-title":"Medical Image Analysis"},{"key":"2024010921480364000_2024.01.03.574115v1.70","first-page":"299","article-title":"SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization","volume":"4","year":"2020","journal-title":"JCO Clinical Cancer Informatics"},{"issue":"10","key":"2024010921480364000_2024.01.03.574115v1.71","doi-asserted-by":"crossref","first-page":"3041","DOI":"10.1002\/hbm.24579","article-title":"Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering","volume":"40","year":"2019","journal-title":"Human Brain Mapping"},{"key":"2024010921480364000_2024.01.03.574115v1.72","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.neuroimage.2018.06.027","article-title":"An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan","volume":"179","year":"2018","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.73","doi-asserted-by":"crossref","first-page":"117063","DOI":"10.1016\/j.neuroimage.2020.117063","article-title":"Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification","volume":"220","year":"2020","journal-title":"NeuroImage"},{"key":"2024010921480364000_2024.01.03.574115v1.74","doi-asserted-by":"crossref","unstructured":"Zhang, F. , Xue, T. , Cai, W. , Rathi, Y. , Westin, C.-F. , & O\u2019Donnell, L. J . (2022). TractoFormer: A Novel Fiber-Level Whole Brain Tractography Analysis Framework Using Spectral Embedding and Vision Transformers. Medical Image Computing and Computer-Assisted Intervention (MICCAI ), 196\u2013206.","DOI":"10.1007\/978-3-031-16431-6_19"},{"key":"2024010921480364000_2024.01.03.574115v1.75","doi-asserted-by":"crossref","first-page":"101993","DOI":"10.1016\/j.media.2021.101993","article-title":"Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19","volume":"70","year":"2021","journal-title":"Medical Image Analysis"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2024.01.03.574115","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T05:45:22Z","timestamp":1768455922000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2024.01.03.574115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,4]]},"references-count":75,"URL":"https:\/\/doi.org\/10.1101\/2024.01.03.574115","relation":{"is-preprint-of":[{"id-type":"doi","id":"10.1002\/hbm.70071","asserted-by":"subject"}]},"subject":[],"published":{"date-parts":[[2024,1,4]]},"subtype":"preprint"}}