Cargando…

Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology

Purpose: White-matter tract segmentation in patients with brain pathology can guide surgical planning and can be used for tissue integrity assessment. Recently, TractSeg was proposed for automatic tract segmentation in healthy subjects. The aim of this study was to assess the use of TractSeg for cor...

Descripción completa

Detalles Bibliográficos
Autores principales: Moshe, Yael H., Ben Bashat, Dafna, Hananis, Zeev, Teicher, Mina, Artzi, Moran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630891/
https://www.ncbi.nlm.nih.gov/pubmed/36320179
http://dx.doi.org/10.1177/15330338221131387
_version_ 1784823705375866880
author Moshe, Yael H.
Ben Bashat, Dafna
Hananis, Zeev
Teicher, Mina
Artzi, Moran
author_facet Moshe, Yael H.
Ben Bashat, Dafna
Hananis, Zeev
Teicher, Mina
Artzi, Moran
author_sort Moshe, Yael H.
collection PubMed
description Purpose: White-matter tract segmentation in patients with brain pathology can guide surgical planning and can be used for tissue integrity assessment. Recently, TractSeg was proposed for automatic tract segmentation in healthy subjects. The aim of this study was to assess the use of TractSeg for corticospinal-tract (CST) segmentation in a large cohort of patients with brain pathology and to evaluate its consistency in repeated measurements. Methods: A total of 649 diffusion-tensor-imaging scans were included, of them: 625 patients and 24 scans from 12 healthy controls (scanned twice for consistency assessment). Manual CST labeling was performed in all cases, and by 2 raters for the healthy subjects. Segmentation results were evaluated based on the Dice score. In order to evaluate consistency in repeated measurements, volume, Fractional Anisotropy (FA), and Mean Diffusivity (MD) values were extracted and correlated for the manual versus automatic methods. Results: For the automatic CST segmentation Dice scores of 0.63 and 0.64 for the training and testing datasets were obtained. Higher consistency between measurements was detected for the automatic segmentation, with between measurements correlations of volume = 0.92/0.65, MD = 0.94/0.75 for the automatic versus manual segmentation. Conclusions: The TractSeg method enables automatic CST segmentation in patients with brain pathology. Superior measurements consistency was detected for the automatic in comparison to manual fiber segmentation, which indicates an advantage when using this method for clinical and longitudinal studies.
format Online
Article
Text
id pubmed-9630891
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-96308912022-11-04 Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology Moshe, Yael H. Ben Bashat, Dafna Hananis, Zeev Teicher, Mina Artzi, Moran Technol Cancer Res Treat Novel applications of Artificial Intelligence in cancer research Purpose: White-matter tract segmentation in patients with brain pathology can guide surgical planning and can be used for tissue integrity assessment. Recently, TractSeg was proposed for automatic tract segmentation in healthy subjects. The aim of this study was to assess the use of TractSeg for corticospinal-tract (CST) segmentation in a large cohort of patients with brain pathology and to evaluate its consistency in repeated measurements. Methods: A total of 649 diffusion-tensor-imaging scans were included, of them: 625 patients and 24 scans from 12 healthy controls (scanned twice for consistency assessment). Manual CST labeling was performed in all cases, and by 2 raters for the healthy subjects. Segmentation results were evaluated based on the Dice score. In order to evaluate consistency in repeated measurements, volume, Fractional Anisotropy (FA), and Mean Diffusivity (MD) values were extracted and correlated for the manual versus automatic methods. Results: For the automatic CST segmentation Dice scores of 0.63 and 0.64 for the training and testing datasets were obtained. Higher consistency between measurements was detected for the automatic segmentation, with between measurements correlations of volume = 0.92/0.65, MD = 0.94/0.75 for the automatic versus manual segmentation. Conclusions: The TractSeg method enables automatic CST segmentation in patients with brain pathology. Superior measurements consistency was detected for the automatic in comparison to manual fiber segmentation, which indicates an advantage when using this method for clinical and longitudinal studies. SAGE Publications 2022-11-01 /pmc/articles/PMC9630891/ /pubmed/36320179 http://dx.doi.org/10.1177/15330338221131387 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Novel applications of Artificial Intelligence in cancer research
Moshe, Yael H.
Ben Bashat, Dafna
Hananis, Zeev
Teicher, Mina
Artzi, Moran
Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title_full Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title_fullStr Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title_full_unstemmed Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title_short Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology
title_sort utilizing the tractseg tool for automatic corticospinal tract segmentation in patients with brain pathology
topic Novel applications of Artificial Intelligence in cancer research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630891/
https://www.ncbi.nlm.nih.gov/pubmed/36320179
http://dx.doi.org/10.1177/15330338221131387
work_keys_str_mv AT mosheyaelh utilizingthetractsegtoolforautomaticcorticospinaltractsegmentationinpatientswithbrainpathology
AT benbashatdafna utilizingthetractsegtoolforautomaticcorticospinaltractsegmentationinpatientswithbrainpathology
AT hananiszeev utilizingthetractsegtoolforautomaticcorticospinaltractsegmentationinpatientswithbrainpathology
AT teichermina utilizingthetractsegtoolforautomaticcorticospinaltractsegmentationinpatientswithbrainpathology
AT artzimoran utilizingthetractsegtoolforautomaticcorticospinaltractsegmentationinpatientswithbrainpathology