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Machine-Based Rejection of Low-Quality Spectra and Estimation of Brain Tumor Probabilities from Magnetic Resonance Spectroscopic Images., , , , and . Bildverarbeitung für die Medizin, page 31-35. Springer, (2006)Overview of the VISCERAL Challenge at ISBI 2015., , , , , , , , , and 9 other author(s). VISCERAL Challenge@ISBI, volume 1390 of CEUR Workshop Proceedings, page 6-11. CEUR-WS.org, (2015)Segmentation of Skeleton and Organs in Whole-Body CT Images via Iterative Trilateration., , , , , , and . IEEE Trans. Med. Imaging, 36 (11): 2276-2286 (2017)Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images., , , , , , and . CoRR, (2018)Probabilistic model for 3D interactive segmentation., , , , , , , , and . Computer Vision and Image Understanding, (2016)Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM., , , and . CVPR Workshops, page 96. IEEE Computer Society, (2006)Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation., , , , and . ISBI, page 1142-1145. IEEE, (2013)Spatially Adaptive Random Forests., , and . ISBI, page 1344-1347. IEEE, (2013)Spatio-temporal MRI reconstruction by enforcing local and global regularity via dynamic total variation and nuclear norm minimization., , , , and . ISBI, page 306-309. IEEE, (2016)Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force., , , , , and . Medical Imaging: Computer-Aided Diagnosis, volume 9785 of SPIE Proceedings, page 978502. SPIE, (2016)