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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2022
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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 |
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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 |
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