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.

翻译：在这项工作中,我们比较了五个深层次的学习解决方案,以自动分割手术后磁共振分流的剖析孔。提议的方法以同样的3D U-Net结构为基础。我们使用一个关于手术后磁共振分流量的数据集,每个数据包括四个磁共振分流序列和相应的剖析孔的地面真实性。有四个解决方案经过不同的磁共振分流序列培训。此外,还介绍了一种使用所有可用序列设计的方法。我们的实验显示,仅用T1加权对比增强式磁共振分流序列培训的方法才能取得最佳结果,DICE中位指数为0.81。