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Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?, , , and . CoRR, (2020)Impact of individual rater style on deep learning uncertainty in medical imaging segmentation., , and . CoRR, (2021)Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers, , , , , , , , , and 81 other author(s). Scientific Data, (August 2021)Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks., , , , , , , , , and 42 other author(s). CoRR, (2018)Generic acquisition protocol for quantitative MRI of the spinal cord, , , , , , , , , and 81 other author(s). Nature Protocols, 16 (10): 4611-4632 (August 2021)ivadomed: A Medical Imaging Deep Learning Toolbox., , , , , , , and . J. Open Source Softw., 6 (58): 2868 (2021)Multiclass Spinal Cord Tumor Segmentation on MRI with Deep Learning., , , , , , and . CoRR, (2020)Benefits of Linear Conditioning for Segmentation using Metadata., , , , , and . MIDL, volume 143 of Proceedings of Machine Learning Research, page 416-430. PMLR, (2021)Automatic spinal cord localization, robust to MRI contrasts using global curve optimization., , , , , , , , , and 2 other author(s). Medical Image Anal., (2018)OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization., , , , , , , , , and 2 other author(s). MICCAI (2), volume 10434 of Lecture Notes in Computer Science, page 712-719. Springer, (2017)