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Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging., , , , , and . CoRR, (2018)Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks., , , , , and . Comput. Biol. Medicine, (2019)SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI., , , , , and . CoRR, (2018)A Framework for Jointly Assessing and Reducing Imaging Artefacts Automatically Using Texture Analysis and Total Variation Optimisation for Improving Perivascular Spaces Quantification in Brain Magnetic Resonance Imaging., , , , , , , , , and 1 other author(s). MIUA, volume 1248 of Communications in Computer and Information Science, page 171-183. Springer, (2020)Deep learning for atrophy quantification in brain magnetic resonance imaging.. University of Girona, Spain, (2020)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)