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MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks., , , , , and . BrainLes@MICCAI, volume 10670 of Lecture Notes in Computer Science, page 204-215. Springer, (2017)Multimodal MRI brain tumor segmentation using random forests with features learned from fully convolutional neural network., , , , and . CoRR, (2017)Validation of an optical flow algorithm to measure blood flow waveforms in arteries using dynamic digital x-ray images., , , , and . Medical Imaging: Image Processing, volume 3979 of SPIE Proceedings, SPIE, (2000)In-Vitro Validation of a Novel Model-Based Approach to the Measurement of Arterial Blood Flow Waveforms from Dynamic Digital X-ray Images., , , , and . MICCAI, volume 2208 of Lecture Notes in Computer Science, page 291-300. Springer, (2001)Automatic 3D Segmentation of the Liver from Computed Tomography Images, a Discrete Deformable Model Approach., , , and . ICARCV, page 1-6. IEEE, (2006)Level Set Snake Algorithms on the Fetal Heart., , , and . ISBI, page 864-867. IEEE, (2007)MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks., , , , , and . CoRR, (2019)Classification of audio signals using statistical features on time and wavelet transform domains., , , , and . ICASSP, page 3621-3624. IEEE, (1998)The Application of a Statistical Shape Model to Diaphragm Tracking in Respiratory-Gated Cardiac PET Images., , , , , and . Proc. IEEE, 97 (12): 2039-2052 (2009)Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI., , , , , , , and . Int. J. Comput. Assist. Radiol. Surg., 12 (2): 183-203 (2017)