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Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery.

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Paper

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The authors' code

Python · 266 lines · 8.9 KB · no license

  1. from flask import Flask, render_template, request, redirect, url_for
  2. import os, sys
  4. sys.path.insert(1, '../scripts')
  5. from calculate_resected_volumes import calc_resec_vol
  6. from generate_mask import gen_mask
  7. from pre2post import pre2post
  8. from pre2post_deformable import pre2post_deformable
  9. from register_atlas_to_preop import register_atlas_to_preop
  11. from jinja2 import Template
  12. from flask import Markup
  13. import sqlite3
  14. import pdfkit
  15. import time
  16. import base64
  18. app = Flask(__name__)
  19. app.secret_key = b'sdfpow23'
  21. import tensorflow as tf
  22. graph = tf.compat.v1.get_default_graph()
  24. @app.route('/')
  25. def index():
  26. return render_template('index.html')
  28. @app.route('/vol', methods=['GET', 'POST'])
  29. def vol():
  30. if request.method == 'POST':
  31. tic = time.perf_counter()
  33. # [img, mask, atl, atl_map] = request.files.getlist("file")
  34. [img, mask] = request.files.getlist("file")
  35. atlas = request.form['atlas']
  37. print([img, mask])
  39. for uploaded_file in [img, mask]:
  40. if uploaded_file.filename != '':
  41. uploaded_file.save(os.path.join("static", uploaded_file.filename))
  43. if atlas == "AAL":
  44. atlas_nii = "tmp/atlas2post_AAL116_origin_MNI_T1old.nii"
  45. atlas_txt = "tmp/AAL116.txt"
  47. df, imgs = calc_resec_vol(
  48. os.path.join("static", img.filename),
  49. os.path.join("static", mask.filename),
  50. # os.path.join("static", atl.filename),
  51. # os.path.join("static", atl_map.filename),
  52. atlas_nii,
  53. atlas_txt,
  54. os.path.join("static", ""),
  55. )
  57. df.sort_values(by="Remaining (%)", inplace=True)
  59. table_html = df.to_html(
  60. table_id='report',
  61. float_format=lambda x: '{0:.2f}'.format(x),
  62. index=False,
  63. justify="center"
  64. )
  66. toc = time.perf_counter()
  68. return redirect(url_for('report', vol_table=Markup(table_html), vol_imgs=imgs, post_op_path=img.filename, mask_path=mask.filename))
  69. return render_template('calculate_vol.html')
  71. @app.route('/feedback', methods=['GET', 'POST'])
  72. def feedback():
  73. if request.method == 'POST':
  74. try:
  75. rating = request.form['rating']
  76. comments = request.form['comments']
  78. print(rating, comments)
  80. with sqlite3.connect('database.db') as con:
  81. cur = con.cursor()
  82. print('test')
  84. cur.execute("INSERT INTO feedback (date_time,rating,comments) VALUES (datetime('now'),?,?)",(rating,comments) )
  86. con.commit()
  87. msg = "Record successfully added"
  88. print(msg)
  89. except Exception as e:
  90. print(e)
  91. con.rollback()
  92. msg = "error in insert operation"
  94. finally:
  95. return redirect(url_for("index"))
  96. con.close()
  98. @app.route('/report', methods=['GET', 'POST'])
  99. def report():
  100. vol_table = request.args.get('vol_table')
  101. vol_imgs = request.args.get('vol_imgs')
  102. post_op_path = request.args.get('post_op_path')
  103. mask_path = request.args.get('mask_path')
  105. HTML_TEMPLATE = '''
  106. <!doctype html>
  107. <html lang="en">
  108. <head>
  109. <!-- Required meta tags -->
  110. <meta charset="utf-8">
  111. <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
  113. <!-- Bootstrap CSS -->
  114. <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">
  116. <title>DeepResection Report</title>
  117. </head>
  119. <body>
  122. <!-- Viewer -->
  123. <div class="background-box container">
  124. <div class="page-header">
  125. <h1>DeepResection Report</h1>
  126. </div>
  128. <div class="row">
  129. <div class="table table-striped">
  130. {vol_table}
  131. </div>
  132. </div>
  133. <div class="row">
  134. <img src="data:image/png;base64, {vol_imgs}"/>
  135. </div>
  136. </div>
  138. </body>
  139. </html>
  140. '''
  142. img = base64.b64encode(open("./static/resection_views.png", "rb").read()).decode()
  144. options = {'enable-local-file-access': None}
  145. pdf = pdfkit.from_string(HTML_TEMPLATE.format(vol_table=Markup(vol_table), vol_imgs=str(img)), './static/report.pdf', css='./static/css/style.css', options=options)
  147. print(os.listdir("tmp"))
  148. if request.method == 'POST':
  149. try:
  150. # rendered_template_enc = rendered_template.encode('utf-8')
  151. vol_table = request.args.get('vol_table')
  152. vol_imgs = request.args.get('vol_imgs')
  153. print("Print Test")
  154. print("Vol ", vol_table)
  155. # rendered_html_for_pdf = render_template('report_pdf.html', vol_table=Markup(vol_table), vol_imgs=vol_imgs)
  157. css = ['./static/css/style.css']
  158. options = {'enable-local-file-access': None}
  159. pdf = pdfkit.from_string(HTML_TEMPLATE.format(vol_table=Markup(vol_table), vol_imgs=vol_imgs), 'report.pdf', css=css, options=options)
  161. return render_template('report.html', vol_table=Markup(vol_table), post_op_path=post_op_path, mask_path=mask_path)
  162. except Exception as e:
  163. print(e)
  164. return render_template('report.html', vol_table=Markup(vol_table), post_op_path=post_op_path, mask_path=mask_path)
  166. @app.route('/mask', methods=['GET', 'POST'])
  167. def mask():
  168. if request.method == 'POST':
  169. tic = time.perf_counter()
  170. patient_id = request.form['id']
  171. atlas = request.form['atlas']
  172. isContinuous = 'continuous' in request.form
  173. # postop = request.files['file']
  175. if len(request.files.getlist("file")) == 2:
  176. [preop, postop] = request.files.getlist("file")
  177. elif len(request.files.getlist("file")) == 3:
  178. [preop, postop, mask] = request.files.getlist("file")
  179. mask.save(os.path.join("static", mask.filename))
  181. preop.save(os.path.join("static", preop.filename))
  182. postop.save(os.path.join("static", postop.filename))
  184. isDeformable = "deformable" in request.form
  185. # Deprecated
  186. # if atlas == "AAL":
  187. # atlas_nii = "tmp/atlas2post_AAL116_origin_MNI_T1.nii"
  188. # atlas_txt = "tmp/AAL116.txt"
  190. if atlas == 'DKT':
  191. atlas_nii = None
  192. atlas_txt = "tmp/dkt_atlas_mappings.txt"
  194. mask_name="{}_predicted_mask.nii.gz".format(patient_id)
  196. output_dir = "static"
  197. global graph
  199. # apply an atlas to pre-operative image, register atlas to post-operative image
  200. print('pre2post')
  201. print("deformable? {}".format(isDeformable))
  202. if isDeformable:
  203. pre2post_deformable(
  204. patient_id,
  205. os.path.join(output_dir, preop.filename),
  206. os.path.join(output_dir, postop.filename),
  207. output_dir,
  208. os.path.join(output_dir, mask.filename)
  209. )
  211. else:
  212. pre2post(
  213. patient_id,
  214. os.path.join(output_dir, preop.filename),
  215. os.path.join(output_dir, postop.filename),
  216. output_dir
  217. )
  219. pre2post_fname = "pre2post_{}".format(preop.filename)
  221. print("register_atlas_to_preop")
  222. registered_atlas_fname = register_atlas_to_preop(
  223. patient_id,
  224. os.path.join(output_dir, pre2post_fname),
  225. output_dir
  226. )
  228. print("gen_mask")
  229. with graph.as_default():
  230. gen_mask(
  231. os.path.join(output_dir, postop.filename),
  232. output_dir,
  233. mask_name,
  234. isContinuous
  235. )
  237. print("calc_resec_volume")
  238. df, imgs = calc_resec_vol(
  239. os.path.join(output_dir, postop.filename),
  240. os.path.join(output_dir, mask_name),
  241. os.path.join(output_dir, registered_atlas_fname),
  242. atlas_txt,
  243. output_dir,
  244. )
  247. df.sort_values(by="Remaining (%)", inplace=True)
  249. table_html = df.to_html(
  250. table_id='report',
  251. float_format=lambda x: '{0:.2f}'.format(x),
  252. index=False,
  253. justify="center"
  254. )
  255. toc = time.perf_counter()
  256. print("Time elapsed: {:.4f}".format(toc - tic))
  258. return redirect(url_for('report', vol_table=Markup(table_html), vol_imgs=imgs, post_op_path=postop.filename, mask_path=mask_name))
  259. return render_template('generate_mask.html')
  261. @app.route('/about')
  262. def about():
  263. return render_template('about.html')
  265. if __name__ == '__main__':
  266. app.run(debug=True, threaded=False)

app.py at commit 8fb625d, no license · at the source

Overview

Authors: Raphael Fernandes Casseb1, Brunno Machado de Campos1, Wallace Souza Loos2, Marcelo Eduardo Ramos Barbosa1,3, Marina Koutsodontis Machado Alvim1, Gabriel Chagas Lutfala Paulino1, Enrico Ghizoni1, Francesco Pucci4, Samuel Worrell4, Clarissa Lin Yassuda1, Roberto Medeiros de Souza2, Lara Jehi4, Fernando Cendes1
ORCID iDs: Raphael Fernandes Casseb, Brunno Machado de Campos, Wallace Souza Loos, Marcelo Eduardo Ramos Barbosa, Marina Koutsodontis Machado Alvim, Gabriel Chagas Lutfala Paulino, Enrico Ghizoni, Clarissa Lin Yassuda, Roberto Medeiros de Souza, Lara Jehi, Fernando Cendes
  1. Neuroimaging Laboratory, Universidade Estadual de Campinas, Campinas, São Paulo, Brazil
  2. Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Calgary, Alberta, Canada
  3. School of Medical Sciences, Pontifical Catholic University of Campinas, Campinas, São Paulo, Brazil
  4. Department of Neurology, Cleveland Clinic Foundation, Cleveland, Ohio, USA
Institutions: Universidade Estadual de Campinas (UNICAMP) (Brazil); University of Calgary (Canada); Pontifícia Universidade Católica de Campinas (Brazil); Cleveland Clinic (United States)
Journal: Epilepsia, volume 67, issue 8, pages 4207-4218
Dates: received 3 November 2025; accepted 4 May 2026; published online 12 June 2026; in print August 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/epi.70296 · PMID 42284022 · PMCID PMC13525573 · OpenAlex W7164535447
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), epilepsy (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning
Keywords: brain region labeling, nnU‐net, postoperative MRI, ResectVol DL, volumetry
MeSH: Brain*, Deep Learning*, Epilepsy*, Imaging, Three-Dimensional*, Magnetic Resonance Imaging*, Adult, Brain Neoplasms, Female, Humans, Male, Middle Aged, Young Adult (* major topic)
Topic: Advanced Neural Network Applications (Computer Vision and Pattern Recognition, Computer Science), according to OpenAlex
Funding: NIH HHS (R01NS097719); Funda??o de Amparo ? Pesquisa do Estado de S?o Paulo (2013/07559‐3, 2013/07559-3, 2020/00019-7, 2020/00019‐7); NINDS NIH HHS (R01 NS097719); National Institutes of Health (R01NS097719)
Citations: not cited yet (Europe PMC); 35 references in the paper

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 (3/22, 95% confidence interval [CI] = 3%–35%), whereas none was observed for ResectVol DL and DeepResection (0/22, 95% CI = 0%–15%).

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.

Repositories

Its files are read in the Code ↔ Paper reader above.

penn-cnt/DeepResection

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8fb625dbc3e1e89c7cc4306b02a7c9b0ba3e2885, 25 August 2022
Languages: Python (14), MATLAB (6), JavaScript (3), Shell (3)
Size: 202 files, 26 scripts
Software Heritage: not archived
Found in: the text, “Automatic segmentation”
Holds: README, environment (requirements.txt), documentation
Not found: license file, CITATION.cff, tests, continuous integration
Tools: NumPy (9 files), imageio (5 files), NiBabel (5 files), pandas (4 files), ANTs (3 files), Matplotlib (3 files), TensorFlow (3 files), Keras (2 files), Image Processing Toolbox (2 files), scikit-image (2 files), Nilearn (1 file), OpenCV (1 file), Pillow (1 file), scikit-learn (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
27 files
At the source: github.com/penn-cnt/DeepResection

rfcasseb/resectvol_dl

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 550249a28dee9251d3f21119bfebb80410aa63eb, 22 February 2026
Languages: Python (3), Shell (2)
Size: 13 files, 5 scripts
Software Heritage: not archived
Found in: “DATA AVAILABILITY STATEMENT”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NiBabel (2 files), ANTs (1 file), FSL (1 file), Nipype (1 file), nnU-Net (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file), SPM (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/rfcasseb/resectvol_dl/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 31 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

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/rfcasseb/resectvol_dl/. Two anonymized T1‐weighted MR images from the test set are available for readers to test the tool at https://drive.google.com/drive/u/2/folders/18y7ObOy5DYEpQ3fZw5tAxp7NHSUOVhT3.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Publisher: n/a → 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://doi.org/10.1002/epi.70296

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/epi.70296},
url = {https://doi.org/10.1002/epi.70296},
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 - 2026/06/12
VL - 67
IS - 8
SP - 4207
EP - 4218
SN - 0013-9580
PB - Wiley
DO - 10.1002/epi.70296
UR - https://doi.org/10.1002/epi.70296
LA - en
ER -

CSL-JSON

{
"id": "10.1002/epi.70296",
"type": "article-journal",
"title": "Fully automated three-dimensional deep learning-based magnetic resonance imaging segmentation of brain cavities in epilepsy surgery",
"container-title": "Epilepsia",
"author": [
{
"family": "Casseb",
"given": "Raphael Fernandes"
},
{
"family": "de Campos",
"given": "Brunno Machado"
},
{
"family": "Loos",
"given": "Wallace Souza"
},
{
"family": "Barbosa",
"given": "Marcelo Eduardo Ramos"
},
{
"family": "Alvim",
"given": "Marina Koutsodontis Machado"
},
{
"family": "Paulino",
"given": "Gabriel Chagas Lutfala"
},
{
"family": "Ghizoni",
"given": "Enrico"
},
{
"family": "Pucci",
"given": "Francesco"
},
{
"family": "Worrell",
"given": "Samuel"
},
{
"family": "Yassuda",
"given": "Clarissa Lin"
},
{
"family": "de Souza",
"given": "Roberto Medeiros"
},
{
"family": "Jehi",
"given": "Lara"
},
{
"family": "Cendes",
"given": "Fernando"
}
],
"container-title-short": "Epilepsia",
"volume": "67",
"issue": "8",
"page": "4207-4218",
"DOI": "10.1002/epi.70296",
"PMID": "42284022",
"PMCID": "PMC13525573",
"ISSN": "0013-9580",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/epi.70296",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
12
]
]
}
}

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