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Article

Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery

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Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
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Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
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Baystate Children’s Hospital, Springfield, MA 01199, USA
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Epilepsy Surgery Program, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
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Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
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Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
*
Authors to whom correspondence should be addressed.
Academic Editor: Dario Arnaldi
Diagnostics 2022, 12(4), 1017; https://doi.org/10.3390/diagnostics12041017
Received: 1 March 2022 / Revised: 10 April 2022 / Accepted: 13 April 2022 / Published: 18 April 2022
(This article belongs to the Special Issue Brain Imaging in Epilepsy)
Delineation of resected brain cavities on magnetic resonance images (MRIs) of epilepsy surgery patients is essential for neuroimaging/neurophysiology studies investigating biomarkers of the epileptogenic zone. The gold standard to delineate the resection on MRI remains manual slice-by-slice tracing by experts. Here, we proposed and validated a semiautomated MRI segmentation pipeline, generating an accurate model of the resection and its anatomical labeling, and developed a graphical user interface (GUI) for user-friendly usage. We retrieved pre- and postoperative MRIs from 35 patients who had focal epilepsy surgery, implemented a region-growing algorithm to delineate the resection on postoperative MRIs and tested its performance while varying different tuning parameters. Similarity between our output and hand-drawn gold standards was evaluated via dice similarity coefficient (DSC; range: 0–1). Additionally, the best segmentation pipeline was trained to provide an automated anatomical report of the resection (based on presurgical brain atlas). We found that the best-performing set of parameters presented DSC of 0.83 (0.72–0.85), high robustness to seed-selection variability and anatomical accuracy of 90% to the clinical postoperative MRI report. We presented a novel user-friendly open-source GUI that implements a semiautomated segmentation pipeline specifically optimized to generate resection models and their anatomical reports from epilepsy surgery patients, while minimizing user interaction. View Full-Text
Keywords: brain resection; epilepsy surgery; MRI; region growing; image segmentation brain resection; epilepsy surgery; MRI; region growing; image segmentation
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MDPI and ACS Style

Billardello, R.; Ntolkeras, G.; Chericoni, A.; Madsen, J.R.; Papadelis, C.; Pearl, P.L.; Grant, P.E.; Taffoni, F.; Tamilia, E. Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery. Diagnostics 2022, 12, 1017. https://doi.org/10.3390/diagnostics12041017

AMA Style

Billardello R, Ntolkeras G, Chericoni A, Madsen JR, Papadelis C, Pearl PL, Grant PE, Taffoni F, Tamilia E. Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery. Diagnostics. 2022; 12(4):1017. https://doi.org/10.3390/diagnostics12041017

Chicago/Turabian Style

Billardello, Roberto, Georgios Ntolkeras, Assia Chericoni, Joseph R. Madsen, Christos Papadelis, Phillip L. Pearl, Patricia E. Grant, Fabrizio Taffoni, and Eleonora Tamilia. 2022. "Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery" Diagnostics 12, no. 4: 1017. https://doi.org/10.3390/diagnostics12041017

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