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

TL;DR

This study compares five deep learning methods based on 3D U-Net architecture for automatic resection cavity segmentation in postoperative MRI, finding that using only T1 contrast-enhanced MRI yields the best median DICE score of 0.81.

Contribution

It systematically evaluates the performance of deep learning solutions trained on different MRI sequences for resection cavity segmentation.

Findings

T1 weighted contrast-enhanced MRI provides the best segmentation results.

The method trained solely on T1 MRI achieved a median DICE of 0.81.

Using all available sequences did not outperform the single best sequence.

Abstract

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.