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Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study
Journal article   Open access   Peer reviewed

Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study

Jieun Seo, Mathilde Ripart, Helene Kaas, Cornelius Kronlage, Ben Sinclair, Lucy Vivash, Merran R Courtney, Terence J O'Brien, Siby Gopinath, Harilal Parasuram, …
Epilepsia (Copenhagen)
08/19/2026
DOI: 10.1002/epi.70450
PMID: 42613992
url
https://doi.org/10.1002/epi.70450View
Published (Version of record) Open Access

Abstract

Quantitative assessment of extent of tissue resection following epilepsy surgery requires accurate delineation of the resection cavity on postoperative magnetic resonance imaging (MRI). Current methods for resection cavity masking are time-consuming and labor-intensive, and existing automated approaches exhibit variable segmentation accuracy, particularly on extratemporal resections. We developed MELD-PostOp, a deep learning tool trained and evaluated on a large, heterogeneous cohort to automatically segment resection cavities. The study included 1.5- and 3T postoperative three-dimensional T1-weighted MRI images from the Multicentre Epilepsy Lesion Detection (MELD) project (n  = 969, 27 centers) and from the EPISURG dataset (n = 133). The cohort included children and adults, alongside a range of resection locations, pathologies, and MRI characteristics. Resection cavities were individually segmented in 285 subjects and used to train an nnU-Net prototype model. The prototype model was used to generate an additional 680 resection masks, which were subsequently quality-controlled, edited, and combined with the original 285 to train the final MELD-PostOp model (n = 965). A Stratified Test Cohort (n = 50) and Independent Test Cohort (n = 87) were withheld for model evaluation. Performance was evaluated using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), number of predicted clusters, and inference runtime, and compared against established tools (Epic-CHOP, ResectVol, and RESSEG). MELD-PostOp achieved a median DSC of .85 and HD95 of 3.61 on the combined test cohort, outperforming Epic-CHOP (DSC .69, HD95 9.67), ResectVol (DSC .66, HD95 15.05), and RESSEG (DSC .43, HD95 32.67), with significant improvements seen in both temporal and especially extratemporal resections. The model detected 98.5% (135/137) of resection cavities. MELD-PostOp runtime was 17 s per MRI, compared to 612 s (ResectVol), 3205 s (Epic-CHOP), and 4 s (RESSEG). MELD-PostOp performance remained high across clinical and imaging subgroups (median DSC > .8). MELD-PostOp is an open-source research tool that provides an accurate, efficient, and generalizable solution for postoperative resection cavity segmentation using only postoperative MRI scans.
 epilepsy surgery segmentation focal epilepsy MRI artificial intelligence

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