论文标题
术后MRI量（包括纵向采集）中不同自动溶液的切除腔分割的比较
Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions
论文作者
论文摘要
在这项工作中，我们比较了五种深度学习解决方案，以自动分割术后MRI中的切除腔。所提出的方法基于相同的3D U-NET体系结构。我们使用术后MRI量的数据集，每件量包括四个MRI序列和相应切除腔的地面真相。用不同的MRI序列训练了四种溶液。此外，还提供了一种使用所有可用序列设计的方法。我们的实验表明，仅使用T1加权对比度增强的MRI序列训练的方法可实现最佳结果，中位骰子指数为0.81。
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.
