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arXiv · 2210.07806

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

Luca Canalini·Jan Klein·Nuno Pedrosa de Barros·Diana Maria Sima·Dorothea Miller·Horst Hahn
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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.

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Luca Canalini, Jan Klein, Nuno Pedrosa de Barros, Diana Maria Sima, Dorothea Miller, Horst Hahn. 2022-10-14. Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions. https://doi.org/10.1117/12.2580889

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