Automatic, deep-learning-based segmentation of the amygdalohippocampectomy resection cavity in MR images

Dingjie Su; Danika L. Paulo; Tyler Ball; Victoria L. Morgan; Dario J. Englot; Benoit M. Dawant

doi:10.1117/12.2654300

3 April 2023 Automatic, deep-learning-based segmentation of the amygdalohippocampectomy resection cavity in MR images

Dingjie Su, Danika L. Paulo, Tyler Ball, Victoria L. Morgan, Dario J. Englot, Benoit M. Dawant

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 1246610 (2023) https://doi.org/10.1117/12.2654300
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Selective amygdalohippocampectomy (SelAH) for mesial temporal lobe epilepsy (mTLE) involves the resection of the anterior hippocampus and the amygdala. A recent study related to SelAH reports that among 168 patients for whom two-year Engel outcomes data were available, 73% had Engel I outcomes (free of disabling seizure); 16.6% had Engel II outcomes (rare disabling seizures); 4.7% had Engel III outcomes (worthwhile improvement); and 5.3% had Engel IV outcomes (no worthwhile improvement). Success rate among sites also varies greatly. Possible explanations for variability in outcomes are the resected volume and/or the subregion of the hippocampus and amygdala that have been resected. To explore this hypothesis, the accurate segmentation of the resected cavity needs to be performed on a large scale. This is, however, a difficult and time-consuming task that requires expertise. Here we explore using a nnUNET to perform the task. Inspired by Youngeun, a level set loss is used in addition to the original DICE and cross-entropy loss in nnUNET to capture the cavity boundaries better. We show that, even with a modest-sized training set (25 volumes), the median DICE value between automated and manual segmentations is 0.88, which suggests that the automatic and accurate segmentation of the resection cavity is achievable.

Conference Presentation

Citation Download Citation

Dingjie Su, Danika L. Paulo, Tyler Ball, Victoria L. Morgan, Dario J. Englot, and Benoit M. Dawant "Automatic, deep-learning-based segmentation of the amygdalohippocampectomy resection cavity in MR images", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 1246610 (3 April 2023); https://doi.org/10.1117/12.2654300

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