Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.
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Overview
- Neuroimaging Laboratory, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
- Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Calgary, Alberta, Canada
- School of Medical Sciences, Pontifical Catholic University of Campinas, Campinas, São Paulo, Brazil
- Department of Neurology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
Abstract
Objective: There are several clinical and research applications for determining the amount of brain tissue resected after epilepsy surgery; however, manual segmentation of postoperative magnetic resonance imaging (MRI) is imprecise and time‐consuming. In this study, we developed and benchmarked ResectVol DL, a freely available deep learning‐based tool that performs this task automatically.
Methods: To create ResectVol DL, we trained a UNet‐like deep learning model using postoperative T1‐weighted MRI from epilepsy surgery patients and evaluated it against manual delineations (ground truth). ResectVol DL was also compared with three automated methods (ResectVol 1.1.2, DeepResection, and Auto3DSeg) using Dice similarity coefficient (DSC), Pearson correlation coefficient, and relative volume difference from manual segmentation. To assess false‐positive detections and generalizability beyond epilepsy, we additionally processed images from healthy controls (no resection) and brain tumor cases.
Results: The final epilepsy cohort comprised 120 patients (57 women, mean age at surgery = 31.5 ± 15.9 [SD] years), split into training (n = 72) and test (n = 48) sets. An additional 42 images (22 healthy controls and 20 brain tumor cases) were included to test for false positives and generalizability. Segmentation performance differed across methods (Friedman test, p < .001). ResectVol Dl achieved the highest median DSC (.925), significantly outperforming ResectVol 1.1.2, DeepResection, and Auto3DSeg after Bonferroni correction. Volume‐based metrics were similar for Auto3DSeg and ResectVol DL (r = .988, relative difference = 8.4% vs. r = .985, 8.1%; no significant difference), yet Auto3DSeg produced three false‐positive cavities in no‐surgery controls (3/
Significance: ResectVol DL provides accurate, fully automated segmentation of postoperative resection cavities, offering a robust and reproducible methodological tool for large‐scale postoperative imaging studies in epilepsy surgery. ResectVol DL also provides volumetric information derived from region labeling, which may serve as potential input for predictive models associated with surgery outcome; however, this application has not yet been validated.
Reproduced under the paper's license (CC BY), from the paper cited above.
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penn-cnt/DeepResection
8fb625dbc3e1e89c7cc4306b02a7c9b0ba3e2885, 25 August 2022Availability: 1 check, the latest on 27 September 2026: the link answers
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static/ — JavaScript, 394 lines, shown from its sourcepapaya.js - flask/
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brain_viewer.py — Python, 3 lines, shown from its source - scripts/
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register_atlas_to_preop. — Python, 30 lines, shown from its sourcepy - scripts/
report_pdf.py — Python, 26 lines, shown from its source - scripts/
subject2colab.m — MATLAB, 54 lines, shown from its source - scripts/
train_GPU.py — Python, 163 lines, shown from its source - scripts/
vol_report.py — Python, 26 lines, shown from its source - README.md — Text, 101 lines, shown from its source
rfcasseb/resectvol_dl
550249a28dee9251d3f21119bfebb80410aa63eb, 22 February 2026Availability: 1 check, the latest on 27 September 2026: the link answers
- 27 September 2026: the link answers
7 files
- resectvol_dl.sh — Shell, 325 lines
- rvdl_labeling.sh — Shell, 225 lines
- rvdl_labeling_descrip.py
— Python, 246 lines - rvdl_replace_header.py — Python, 112 lines
- rvdl_tissue_seg.py — Python, 54 lines
- LICENSE — License, 28 lines
- README.md — Text, 177 lines
The paper's code and data availability statement is in the Data section.
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Data
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Data availability statement
The data supporting the findings of this study can be made available upon approval from the ethics committee and through a formal data‐sharing agreement. ResectVol DL is available at github.com/
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 2, 28 September 2026
- Publisher: — → Wiley
Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 12 MeSH terms, 4 funders, 24 references.
Cite
This paper
Casseb, R. F., de Campos, B. M., Loos, W. S., Barbosa, M. E. R., Alvim, M. K. M., Paulino, G. C. L., Ghizoni, E., Pucci, F., Worrell, S., Yassuda, C. L., de Souza, R. M., Jehi, L., & Cendes, F. (2026). Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery. Epilepsia, 67(8), 4207-4218. https://
BibTeX
@article{casseb2026fully
author = {Casseb, Raphael Fernandes and de Campos, Brunno Machado and Loos, Wallace Souza and Barbosa, Marcelo Eduardo Ramos and Alvim, Marina Koutsodontis Machado and Paulino, Gabriel Chagas Lutfala and Ghizoni, Enrico and Pucci, Francesco and Worrell, Samuel and Yassuda, Clarissa Lin and de Souza, Roberto Medeiros and Jehi, Lara and Cendes, Fernando},
title = {{Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery}},
journal = {Epilepsia},
year = {2026},
month = jun,
volume = {67},
number = {8},
pages = {4207--4218},
publisher = {Wiley},
issn = {0013-9580},
doi = {10.1002/
url = {https://
pmid = {42284022},
pmcid = {PMC13525573}
}
RIS
TY - JOUR
AU - Casseb, Raphael Fernandes
AU - de Campos, Brunno Machado
AU - Loos, Wallace Souza
AU - Barbosa, Marcelo Eduardo Ramos
AU - Alvim, Marina Koutsodontis Machado
AU - Paulino, Gabriel Chagas Lutfala
AU - Ghizoni, Enrico
AU - Pucci, Francesco
AU - Worrell, Samuel
AU - Yassuda, Clarissa Lin
AU - de Souza, Roberto Medeiros
AU - Jehi, Lara
AU - Cendes, Fernando
TI - Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery
T2 - Epilepsia
J2 - Epilepsia
PY - 2026
DA - 2026/
VL - 67
IS - 8
SP - 4207
EP - 4218
SN - 0013-9580
PB - Wiley
DO - 10.1002/
UR - https://
LA - en
ER -
CSL-JSON
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