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Article

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

by
Roberto Billardello
1,2,*,
Georgios Ntolkeras
1,3,
Assia Chericoni
1,2,
Joseph R. Madsen
4,
Christos Papadelis
5,
Phillip L. Pearl
6,
Patricia Ellen Grant
1,
Fabrizio Taffoni
2 and
Eleonora Tamilia
1,*
1
Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC), Newborn Medicine Division, Department of Pediatrics, Boston Children’s Hospital, Boston, MA 02115, USA
2
Advanced Robotics and Human-Centered Technologies-CREO Lab, Università Campus Bio-Medico di Roma, 00128 Rome, Italy
3
Baystate Children’s Hospital, Springfield, MA 01199, USA
4
Epilepsy Surgery Program, Department of Neurosurgery, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
5
Jane and John Justin Neurosciences Center, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
6
Division of Epilepsy and Clinical Neurophysiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA
*
Authors to whom correspondence should be addressed.
Diagnostics 2022, 12(4), 1017; https://doi.org/10.3390/diagnostics12041017
Submission 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)
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Abstract

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.
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 Ellen 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

APA Style

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

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