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

doi:10.1117/12.2580889

15 February 2021 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

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 1159822 (2021) https://doi.org/10.1117/12.2580889
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Luca Canalini, Jan Klein, Nuno Pedrosa de Barros, Diana Maria Sima, Dorothea Miller, and Horst Hahn "Comparison of different automatic solutions for resection cavity segmentation in postoperative MRI volumes including longitudinal acquisitions", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 1159822 (15 February 2021); https://doi.org/10.1117/12.2580889

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