  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 with the T1
weighted contrast-enhanced MRI sequence achieves the best results, with a
median DICE index of 0.81.
