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Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions. (arXiv:2210.07806v1 [cs.CV])
Oct. 17, 2022, 1:16 a.m. | Luca Canalini, Jan Klein, Nuno Pedrosa de Barros, Diana Maria Sima, Dorothea Miller, Horst Hahn
cs.CV updates on arXiv.org arxiv.org
In this work, we compare five deep learning solutions to automatically
segment the resection cavity in postoperative MRI. The proposed methods are
based on the same 3D U-Net architecture. We use a dataset of postoperative MRI
volumes, each including four MRI sequences and the ground truth of the
corresponding resection cavity. Four solutions are trained with a different MRI
sequence. Besides, a method designed with all the available sequences is also
presented. Our experiments show that the method trained only …
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