Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions
This work addresses the problem of accurate surgical cavity segmentation in medical imaging for clinicians, but it is incremental as it compares existing methods on a specific dataset.
The researchers compared five deep learning methods for automatically segmenting resection cavities in postoperative MRI scans, finding that a method trained solely on T1 weighted contrast-enhanced MRI sequences performed best with a median DICE index of 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.