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Applying the algorithm ässessing quality using image registration circuits" (AQUIRC) to multi-atlas segmentation., , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 90341F. SPIE, (2014)Hierarchical performance estimation in the statistical label fusion framework., and . Medical Image Analysis, 18 (7): 1070-1081 (2014)Groupwise multi-atlas segmentation of the spinal cord's internal structure., , , , and . Medical Image Analysis, 18 (3): 460-471 (2014)Vascular burden and APOE ε4 are associated with white matter microstructural decline in cognitively normal older adults., , , , , , and . NeuroImage, (2019)Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T., , , , , and . NeuroImage, 36 (4): 1123-1138 (2007)Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks., , , , , , , , , and 1 other author(s). CoRR, (2018)Learning Implicit Brain MRI Manifolds with Deep Learning., , , , , and . CoRR, (2018)Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth., , , , , and . CoRR, (2017)Towards machine learning prediction of deep brain stimulation (DBS) intra-operative efficacy maps., , , , , , , , , and . Medical Imaging: Image Processing, volume 10949 of SPIE Proceedings, page 1094922. SPIE, (2019)Do We Really Need Robust and Alternative Inference Methods for Brain MRI?, , and . MBIA, volume 7509 of Lecture Notes in Computer Science, page 77-93. Springer, (2012)