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Predicting the Location of Glioma Recurrence after a Resection Surgery.

, , , , , and . STIA, volume 7570 of Lecture Notes in Computer Science, page 113-123. Springer, (2012)

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Layered Spatio-temporal Forests for Left Ventricle Segmentation from 4D Cardiac MRI Data., , , and . STACOM, volume 7085 of Lecture Notes in Computer Science, page 109-119. Springer, (2011)Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images., , , , , and . NeuroImage, 57 (2): 378-390 (2011)The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)., , , , , , , , , and 58 other author(s). IEEE Trans. Medical Imaging, 34 (10): 1993-2024 (2015)Brain Tumor Cell Density Estimation from Multi-modal MR Images Based on a Synthetic Tumor Growth Model., , , , , and . MCV, volume 7766 of Lecture Notes in Computer Science, page 273-282. Springer, (2012)Predicting the Location of Glioma Recurrence after a Resection Surgery., , , , , and . STIA, volume 7570 of Lecture Notes in Computer Science, page 113-123. Springer, (2012)Spatially Adaptive Random Forests., , and . ISBI, page 1344-1347. IEEE, (2013)Importance of patient DTI's to accurately model glioma growth using the reaction diffusion equation., , , , and . ISBI, page 1142-1145. IEEE, (2013)Tumor growth parameters estimation and source localization from a unique time point: Application to low-grade gliomas., , , , , , and . Computer Vision and Image Understanding, 117 (3): 238-249 (2013)A Generative Probabilistic Model and Discriminative Extensions for Brain Lesion Segmentation - With Application to Tumor and Stroke., , , , , , , , , and 2 other author(s). IEEE Trans. Med. Imaging, 35 (4): 933-946 (2016)Spatial Decision Forests for MS Lesion Segmentation in Multi-Channel MR Images., , , , , and . MICCAI (1), volume 6361 of Lecture Notes in Computer Science, page 111-118. Springer, (2010)