Myelin water imaging data analysis in less than one minute.
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NeuroImage, 2020
Deep learning of brain lesion patterns and user-defined clinical and MRI features for predicting conversion to multiple sclerosis from clinically isolated syndrome.
Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2019
Hierarchical Multimodal Fusion of Deep-Learned Lesion and Tissue Integrity Features in Brain MRIs for Distinguishing Neuromyelitis Optica from Multiple Sclerosis.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2017, 2017
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.
IEEE Trans. Medical Imaging, 2016
Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis.
Proceedings of the Deep Learning and Data Labeling for Medical Applications, 2016
Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 2016
Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsic and Intrinsic Image Cues.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5, 2015
Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference Munich, Germany, October 5, 2015
Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation.
Proceedings of the Machine Learning in Medical Imaging - 5th International Workshop, 2014
Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014
Myelin water and T<sub>2</sub> relaxation measurements in the healthy cervical spinal cord at 3.0T: Repeatability and changes with age.
NeuroImage, 2011
Optimizing the Use of Radiologist Seed Points for Improved Multiple Sclerosis Lesion Segmentation.
IEEE Trans. Biomed. Eng., 2010
Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment.
Medical Image Anal., 2009
Myelin water imaging of multiple sclerosis at 7 T: Correlations with histopathology.
NeuroImage, 2008
Complementary information from multi-exponential T<sub>2</sub> relaxation and diffusion tensor imaging reveals differences between multiple sclerosis lesions.
NeuroImage, 2008
Reproducibility and reliability of MR measurements in white matter: Clinical implications.
NeuroImage, 2006