Automated segmentation of postsurgical resection cavities on magnetic resonance imaging in focal epilepsy: A Multicentre Epilepsy Lesion Detection study
Year: 2026
Type: article
Abstract: OBJECTIVE: 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. METHODS: = 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). RESULTS: 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). SIGNIFICANCE: 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.
Source: Epilepsia
DOI: 10.1002/epi.70450
Authors: Jieun Seo, Mathilde Ripart, Helene Kaas, Cornelius Kronlage, Ben Sinclair, Lucy Vivash, Merran R. Courtney, Terence J. O'Brien, Siby Gopinath, Harilal Parasuram, Sedat Kandemirli, Natally Alarab, Lillian Lai, Marcus Likeman, Kai Zhang, Jiajie Mo, Georgian Ciobotaru, James Galea, Philip Sequeiros‐Peggs, Khalid Hamandi, Hua Xie, Venkata Sita Priyanka Illapani, William D. Gaillard, Nathan T. Cohen, Alexander G. Weil, Florence Henrichon‐Goulet, Kenza S. Lahlou, Aristides Hadjinicolaou, Agustín Ibáñez, Gonzalo M. Rojas‐Costa, Horst Urbach, Lara Bücheler, Marcel Heers, Adrián Valls Carbó, Rafael Toledano, Giulia Nobile, Costanza Parodi, Domenico Tortora, Alessandro Consales, Antonella Riva, Mariasavina Severino, Martin Tisdall, Felice D'Arco, Kshitij Mankad, Aswin Chari, Maria H. Eriksson, Rory J. Piper, J. Helen Cross, Torsten Baldeweg, Sofia González‐Ortiz, Jose Pariente, Nuria Bargalló, Yawu Liu, Reetta Kälviäinen, Carmen Barba, Matteo Lenge, Renzo Guerrini, Masaki Iwasaki, Daichi Sone, Hiroyuki Maki, Tomoki Imokawa, Noriko Sato, Julien Jung, Francisco Sepulveda, Daniel Mansilla, Andres Goycoolea, Ingeborg Lopez, Antonio Napolitano, Alessandro De Benedictis, Luca De Palma, Maria Camilla Rossi‐Espagnet, Nikolaos Kondylidis, Kostakis Gkiatis, Kyriakos Garganis, Joshua Pepper, Stefano Seri, John S. Duncan, Clarissa L. Yasuda, Lucas Scárdua‐Silva, Marina K. M. Alvim, Fernando Cendes, Antonio G. Gennari, Ruth O'Gorman Tuura, Georgia Ramantani, Mariam Josyula, Joel Stein, Nishant Sinha, Kate Davis, R. Edward Hogan, Luigi Maccotta, Sophie Adler, Konrad Wagstyl
Institutions: King's College London, University College London, Rigshospitalet, University of Tübingen, The Alfred Hospital, Monash University, Amrita Vishwa Vidyapeetham, Boston Children's Hospital, University of Iowa Health Care, Bristol Royal Hospital for Children, Capital Medical University, Beijing Tian Tan Hospital, University Hospital of Wales, Children's National, George Washington University, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Adolfo Ibáñez University, Istanbul Medipol University, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana, Finis Terrae University, Centro de Investigacion Principe Felipe, University Medical Center Freiburg, University of Freiburg, Hospital Ruber Internacional, Istituto Giannina Gaslini, University of Genoa, Great Ormond Street Hospital, Hospital for Sick Children, Fundació Clínic per a la Recerca Biomèdica, Consorci Institut D'Investigacions Biomediques August Pi I Sunyer, Fundació de Recerca Clínic Barcelona-Institut d’Investigacions Biomèdiques August Pi i Sunyer, University of Eastern Finland, Kuopio University Hospital, Meyer Children's Hospital, University of Florence, National Center of Neurology and Psychiatry, Juntendo University, Institute of Science Tokyo, Instituto de Neurocirugía Dr. Alfonso Asenjo, Bambino Gesù Children's Hospital, Istituti di Ricovero e Cura a Carattere Scientifico, St. Luke's Hospital, Birmingham Women’s and Children’s NHS Foundation Trust, Aston University, National Hospital for Neurology and Neurosurgery, UCL Queen Square Institute of Neurology, Universidade Estadual de Campinas (UNICAMP), Brazilian Institute of Neuroscience and Neurotechnology, University Hospital Zurich, University Children's Hospital Zurich, University of Zurich, University of Pennsylvania, Washington University in St. Louis
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,360
Awards: G1067598872, Transforming neurodevelopmental disorders using multi scale imaging and genomics, US-South American Initiative for Genetic-Neural-Behavioral Interactions in Human Neurodegenerative Research, Social epigenetics of Alzheimer's disease and related dementias in Latin American countries, An automated machine learning approach to language changes in Alzheimer’s disease and frontotemporal dementia across Latino and English-speaking populations, Circadian Disturbance and Dementia in Latin America
OpenAlex ID: W7203747591