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Myocardial Infarction Segmentation From Late Gadolinium Enhancement MRI By Neural Networks and Prior Information.

, , , , , , and . IJCNN, page 1-8. IEEE, (2020)

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Semi-automated labelling of medical images: benefits of a collaborative work in the evaluation of prostate cancer in MRI., , , , and . CoRR, (2017)Markovian method for 2D, 3D and 4D segmentation of MRI., , , , and . ICIP, page 3012-3015. IEEE, (2008)GANs for Medical Image Synthesis: An Empirical Study., , and . CoRR, (2021)Segmentation-Free Estimation of Aortic Diameters from MRI Using Deep Learning., , , and . M&Ms and EMIDEC/STACOM@MICCAI, volume 12592 of Lecture Notes in Computer Science, page 166-174. Springer, (2020)Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI., , , , , , , , and . Comput. Medical Imaging Graph., (2022)Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map., , , and . ICIP, page 1185-1189. IEEE, (2015)Comparison of CNN Fusion Strategies for Left Ventricle Segmentation from Multi-modal MRI., , , and . FIMH, volume 13958 of Lecture Notes in Computer Science, page 265-273. Springer, (2023)A 4D Patient-Specific Modelling of the Thoracic Aorta from Cine-MR Images., , , , , and . SITIS, page 269-276. IEEE Computer Society, (2011)Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge., , , , , , , , , and 23 other author(s). Medical Image Anal., (2022)Deep Learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge., , , , , , , , , and 23 other author(s). CoRR, (2021)