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Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography.

Overview

Authors: Bruna Garbes Gonçalves Pinto1, Tayran Milá Mendes Olegário1, Pedro Vinicius Alves Silva1, Gabriel Monteiro Ferracioli1, Artur José Marques Paulo1, Klaus Schumacher1, Mateus Trinconi Cunha1,2, Henrique Min Ho Lee1, Mariana Athaniel Silva Rodrigues1, Felipe Campos Kitamura3,4, Joselisa Peres Queiroz de Paiva1, Rafael Maffei Loureiro1
  1. Image Department, Hospital Israelita Albert Einstein, 05652-000 São Paulo, Brazil
  2. Sunnybrook Health Sciences Centre, M4N 3M5 Toronto, ON Canada
  3. Department of Diagnostic Imaging, Universidade Federal de São Paulo, 04021-001 Sao Paulo, Brazil
  4. Eden, Palo Alto, California USA
Institutions: Hospital Israelita Albert Einstein (Brazil); Sunnybrook Health Science Centre (Canada); Universidade Federal de São Paulo (Brazil)
Journal: Scientific reports, volume 16, issue 1, article 26587
Dates: received 20 August 2024; accepted 16 April 2026; published online 25 August 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-49678-7 · PMID 42637791 · PMCID PMC13503715 · OpenAlex W7204111014
Open access: gold, a free copy (OpenAlex)
Status: code on request
Categories: structural MRI / diffusion (modality), other (modality), human (organism)
Methods: Statistics
Keywords: Deep learning, Artificial intelligence, Brain, Computed tomography, Lateral ventricles, Intracranial volume, Segmentation, Imaging, X-ray tomography, Neurology
MeSH: Brain*, Cerebral Ventricles*, Deep Learning*, Tomography, X-Ray Computed*, Convolutional Neural Networks, Female, Humans, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male (* major topic)
Topic: Cerebrospinal fluid and hydrocephalus (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex
Funding: Program for Support to the Institutional Development of the Unified Health System
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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

Tracing map

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Data

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Code and data availability statement

The paper has a code and data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request
  • it says that the code is available on request

Read it in the paper: doi.org/10.1038/s41598-026-49678-7.

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 10 keywords, 10 MeSH terms, 1 funder, 48 references.

Cite

This paper

Pinto, B. G. G., Olegário, T. M. M., Silva, P. V. A., Ferracioli, G. M., Paulo, A. J. M., Schumacher, K., Cunha, M. T., Lee, H. M. H., Rodrigues, M. A. S., Kitamura, F. C., de Paiva, J. P. Q., & Loureiro, R. M. (2026). Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography. Scientific reports, 16(1), 26587. https://doi.org/10.1038/s41598-026-49678-7

BibTeX

@article{pinto2026clinical,
author = {Pinto, Bruna Garbes Gonçalves and Olegário, Tayran Milá Mendes and Silva, Pedro Vinicius Alves and Ferracioli, Gabriel Monteiro and Paulo, Artur José Marques and Schumacher, Klaus and Cunha, Mateus Trinconi and Lee, Henrique Min Ho and Rodrigues, Mariana Athaniel Silva and Kitamura, Felipe Campos and de Paiva, Joselisa Peres Queiroz and Loureiro, Rafael Maffei},
title = {{Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography}},
journal = {Scientific reports},
year = {2026},
month = aug,
volume = {16},
number = {1},
pages = {26587},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-49678-7},
url = {https://doi.org/10.1038/s41598-026-49678-7},
pmid = {42637791},
pmcid = {PMC13503715}
}

RIS

TY - JOUR
AU - Pinto, Bruna Garbes Gonçalves
AU - Olegário, Tayran Milá Mendes
AU - Silva, Pedro Vinicius Alves
AU - Ferracioli, Gabriel Monteiro
AU - Paulo, Artur José Marques
AU - Schumacher, Klaus
AU - Cunha, Mateus Trinconi
AU - Lee, Henrique Min Ho
AU - Rodrigues, Mariana Athaniel Silva
AU - Kitamura, Felipe Campos
AU - de Paiva, Joselisa Peres Queiroz
AU - Loureiro, Rafael Maffei
TI - Clinical validation pipeline of a deep learning model for segmenting and quantifying intracranial and ventricular volumes on computed tomography
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/08/25
VL - 16
IS - 1
SP - 26587
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-49678-7
UR - https://doi.org/10.1038/s41598-026-49678-7
LA - en
ER -

CSL-JSON

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