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Combining loss functions for deep learning bladder segmentation on dynamic MRI.

, , , and . BHI, page 1-4. IEEE, (2021)

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A Diffeomorphic Matching Based Characterization of the Pelvic Organ Dynamics., , , and . CAIP (1), volume 6854 of Lecture Notes in Computer Science, page 469-476. Springer, (2011)ConText-GAN: using contextual texture information for realistic and controllable medical image synthesis., , , and . BHI, page 1-4. IEEE, (2023)Automatic estimation of pelvic organ anatomical references., , , and . EMBC, page 5124-5127. IEEE, (2011)Four-dimensional reconstruction and characterization of bladder deformations., , and . Comput. Methods Programs Biomed., (July 2023)Uterus segmentation in dynamic MRI using LBP texture descriptors., , , and . Medical Imaging: Image Processing, volume 9034 of SPIE Proceedings, page 90343W. SPIE, (2014)A shape descriptors comparison for organs deformation sequence characterization in MRI sequences., , , and . ICIP, page 1069-1072. IEEE, (2009)Multi-object segmentation framework using deformable models for medical imaging analysis., , , , , and . Medical Biol. Eng. Comput., 54 (8): 1181-1192 (2016)Context-Gan: Controllable Context Image Generation Using Gans., , , , and . ISBI, page 1-5. IEEE, (2023)A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment., , , and . J. Math. Imaging Vis., 47 (1-2): 151-164 (2013)Toward an active three dimensional navigation system in medical imaging., , and . CVRMed, volume 1205 of Lecture Notes in Computer Science, page 337-346. Springer, (1997)