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Automated sub-cortical brain structure segmentation combining spatial and deep convolutional features., , , , , , and . Medical Image Anal., (2018)Generating Longitudinal Atrophy Evaluation Datasets on Brain Magnetic Resonance Images Using Convolutional Neural Networks and Segmentation Priors., , , , , and . Neuroinformatics, 19 (3): 477-492 (2021)Quantitative Analysis of Patch-Based Fully Convolutional Neural Networks for Tissue Segmentation on Brain Magnetic Resonance Imaging., , , , , and . IEEE Access, (2019)A four-dimensional computational model of dynamic contrast-enhanced magnetic resonance imaging measurement of subtle blood-brain barrier leakage., , , , , , , , , and . NeuroImage, (2021)Deep convolutional neural networks for brain image analysis on magnetic resonance imaging: a review., , , , , , and . Artif. Intell. Medicine, (2019)Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge., , , , , , , , , and 41 other author(s). CoRR, (2018)Examining the Relationship between Semiquantitative Methods Analysing Concentration-Time and Enhancement-Time Curves from Dynamic-Contrast Enhanced Magnetic Resonance Imaging and Cerebrovascular Dysfunction in Small Vessel Disease., , , , , , , and . J. Imaging, 6 (6): 43 (2020)Acute and sub-acute stroke lesion segmentation from multimodal MRI., , , , , and . Comput. Methods Programs Biomed., (2020)Evaluating Robustness of Template Matching Algorithms as a Multi-objective Optimisation Problem., , and . CIARP, volume 8827 of Lecture Notes in Computer Science, page 30-37. Springer, (2014)Deep learning for atrophy quantification in brain magnetic resonance imaging.. University of Girona, Spain, (2020)