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Predict brain MR image registration via sparse learning of appearance and transformation.

, , , , and . Medical Image Anal., 20 (1): 61-75 (2015)

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Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests., , , , , and . IEEE Trans. Med. Imaging, 35 (6): 1532-1543 (2016)Learning-based deformable registration of MR brain images., , and . IEEE Trans. Med. Imaging, 25 (9): 1145-1157 (2006)Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes., , , and . IEEE Trans. Med. Imaging, 37 (1): 185-199 (2018)An Adaptive-Focus Deformable Model Using Statistical and Geometric Information., and . IEEE Trans. Pattern Anal. Mach. Intell., 22 (8): 906-913 (2000)Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks., , , , and . CoRR, (2019)Motion Correction Strategies for Interventional Angiography Images: A Comparative Approach., , , , and . ICIP (1), page 497-500. IEEE, (2007)Structure-adaptive B-snake for segmenting complex objects., , and . ICIP (2), page 769-772. IEEE, (2001)MARM: Multiscale Adaptive Regression Models for Neuroimaging Data., , , , and . IPMI, volume 5636 of Lecture Notes in Computer Science, page 314-325. Springer, (2009)Brain-Cloud: A Generalized and Flexible Registration Framework for Brain MR Images., , , and . AE-CAI, volume 8090 of Lecture Notes in Computer Science, page 153-161. Springer, (2013)An Efficient Method for Deformable Segmentation of 3D US Prostate Images., and . MIAR, volume 3150 of Lecture Notes in Computer Science, page 103-112. Springer, (2004)