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Abstract #2579

Automatic 3D bladder segmentation from low-field MR images using 3D U-Net

Lianne Straetemans1, Dieuwertje Alblas2, Lisan M. Morsinkhof1, Jelmer M. Wolterink2, and Frank F.J. Simonis1
1Magnetic Detection & Imaging, TechMed Centre, University of Twente, Enschede, Netherlands, 2Applied Mathematics, TechMed Centre, University of Twente, Enschede, Netherlands


Pelvic organ prolapse (POP) is a common problem in women, but little is known about treatments. Automatic 3D segmentation of pelvic organs would be useful for improving research in this area. This study successfully applies a 3D U-Net for automatic bladder segmentation of upright and supine low-field MRI scans from asymptomatic women. The resulting network will probably also perform well on data from POP patients. Further improvements are expected when the training data is completed. Future work will focus on segmentation of additional pelvic organs.

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