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Abstract #0137

Advanced Deep Learning Models Enhance Piriform Cortex Segmentation for Epilepsy Surgery

Javier Antonio Urriola Yaksic1,2, Leo Lebrat3, Hang Min1, Alex Pagnozzi1, Ying Xia1, Ines Vati1, Charles X. Li4, Senali Weeratunga2, Merran Courney5,6, Jurgen Fripp1, Matthew Gutman2,7, Andrew Neal2,5, Ben Sinclair2, Terrence J. O'Brien2,5,6,8, and DanaKai Bradford1
1Australian E-Health Research Centre, CSIRO, Brisbane, Australia, 2Department of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Australia, 3Data61, CSIRO, Brisbane, Australia, 4Department of Radiology, Alfred Health, Melbourne, Australia, 5Department of Neurology, Alfred Health, Melbourne, Australia, 6Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia, 7Neurosurgery, Alfred Health, Melbourne, Australia, 8Department of Medicine, The University of Melbourne, Melbourne, Australia

Synopsis

Keywords: Epilepsy, Segmentation, Piriform Cortex, Temporal Lobe Epilepsy, Transformer Models

Motivation: The piriform cortex (PC) is emerging as an important structure for surgery for drug-resistant mesial temporal lobe epilepsy, where resection improves postsurgical outcomes. Current segmentation methods remain suboptimal, necessitating advanced solutions.

Goal(s): To compare state-of-the-art deep learning (DL) models and label fusion for automated PC segmentation.

Approach: MRI scans from EPISURG (n=49, epilepsy side balanced) and Hammersmith (n=8 controls, gender balanced) were preprocessed (bias correction, skull-stripping, registration) and evaluated through five-fold cross-validation (70/30 training/validation) using segmentation metrics.

Results: Transformer-inspired/based models achieved the highest accuracy, with MedNeXtL reaching median Dice scores of 0.90 (right) and 0.91 (left), outperforming nnU-Net and label fusion methods.

Impact: Advanced DL models with attention mechanisms, such as MedNeXtL and SwinUNETR, significantly enhance piriform cortex segmentation accuracy. This improvement holds promise for refining surgical margins for patients with drug-resistant temporal lobe epilepsy, potentially leading to better postsurgical outcomes.

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Keywords