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In this work, we compare five deep learning solutions to automaticallysegment the resection cavity in postoperative MRI. The proposed methods arebased on the same 3D U-Net architecture. We use a dataset of postoperative MRIvolumes, each including four MRI sequences and the ground truth of thecorresponding resection cavity. Four solutions are trained with a different MRIsequence. Besides, a method designed with all the available sequences is alsopresented. Our experiments show that the method trained only with the T1weighted contrast-enhanced MRI sequence achieves the best results, with amedian DICE index of 0.81.

Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions