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Disambiguation of "Rathi, Yogesh"

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DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.

, , , , , , , , and . IEEE Trans. Medical Imaging, 43 (3): 1191-1202 (March 2024)

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Yogesh Rathi

Yogesh Bhargava

Anchana Rathinasamy

Klaus Rathing

Prakash Chandra Rathi

 

Other publications of authors with the same name

Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification., , , , , , , , , and 2 other author(s). NeuroImage, (2020)Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography., , , , , , , , , and 2 other author(s). Int. J. Comput. Assist. Radiol. Surg., 11 (8): 1475-1486 (2016)Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods., , , , , , , , , and 1 other author(s). CoRR, (2023)A review of deep learning methods for MRI reconstruction., and . CoRR, (2021)SlicerTMS: Interactive Real-time Visualization of Transcranial Magnetic Stimulation using Augmented Reality and Deep Learning., , , , , , and . CoRR, (2023)Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?, , , , , , , , , and 131 other author(s). NeuroImage, (2021)Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation., , , , , , , and . MICCAI (7), volume 12907 of Lecture Notes in Computer Science, page 497-507. Springer, (2021)TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation., , , , , , , , and . MICCAI (8), volume 14227 of Lecture Notes in Computer Science, page 409-419. Springer, (2023)A Deep-Learning Framework for Estimating the Posterior Distribution of the Standard Model of Diffusion Mri., , and . ISBI, page 1-4. IEEE, (2023)In-Vivo Estimation of Axon Radii From Clinical Scanners., , and . ISBI, page 1-5. IEEE, (2023)