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Fiber tractogrophy and tract segmentation in multiple sclerosis lesions.

, , , , and . ISBI, page 1488-1491. IEEE, (2011)

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Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks., , , , , , , , , and 42 other author(s). CoRR, (2018)Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view., , , , , , and . NeuroImage, (2013)Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes., , , , and . NeuroImage, (2013)A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions., , , , , and . NeuroImage, 49 (2): 1524-1535 (2010)SMORE: A Self-Supervised Anti-Aliasing and Super-Resolution Algorithm for MRI Using Deep Learning., , , , , and . IEEE Trans. Medical Imaging, 40 (3): 805-817 (2021)Information Criteria for Dynamic Contrast-Enhanced Magnetic Resonance Imaging., , , and . PRNI, page 37-41. IEEE, (2013)Impulse response timing differences in BOLD and CBV weighted fMRI., , , , , , and . NeuroImage, (2018)Belief Propagation Based Segmentation of White Matter Tracts in DTI., , , , and . MICCAI (1), volume 5761 of Lecture Notes in Computer Science, page 943-950. Springer, (2009)Population-wide principal component-based quantification of blood-brain-barrier dynamics in multiple sclerosis., , , , and . NeuroImage, 57 (4): 1430-1446 (2011)Topology preserving automatic segmentation of the spinal cord in magnetic resonance images., , , , , and . ISBI, page 1737-1740. IEEE, (2011)