Dr. Danika L. Paulo Profile

Dr. Danika L. Paulo

at Vanderbilt Univ Medical Ctr

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Proceedings Article | 3 April 2023 Presentation + Paper

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

Dingjie Su, Danika Paulo, Tyler Ball, Victoria Morgan, Dario Englot, Benoit Dawant

Proceedings Volume 12466, 1246610 (2023) https://doi.org/10.1117/12.2654300

KEYWORDS: Resection, Image segmentation, Magnetic resonance imaging, Epilepsy, Medical imaging, Neurosurgery, Deep learning

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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.

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