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Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging
White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of stream...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Nature Singapore
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256641/ https://www.ncbi.nlm.nih.gov/pubmed/37017902 http://dx.doi.org/10.1007/s12565-023-00715-9 |
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author | Andica, Christina Kamagata, Koji Aoki, Shigeki |
author_facet | Andica, Christina Kamagata, Koji Aoki, Shigeki |
author_sort | Andica, Christina |
collection | PubMed |
description | White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg. |
format | Online Article Text |
id | pubmed-10256641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer Nature Singapore |
record_format | MEDLINE/PubMed |
spelling | pubmed-102566412023-06-11 Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging Andica, Christina Kamagata, Koji Aoki, Shigeki Anat Sci Int Review Article White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg. Springer Nature Singapore 2023-04-05 2023 /pmc/articles/PMC10256641/ /pubmed/37017902 http://dx.doi.org/10.1007/s12565-023-00715-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Andica, Christina Kamagata, Koji Aoki, Shigeki Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title | Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title_full | Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title_fullStr | Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title_full_unstemmed | Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title_short | Automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
title_sort | automated three-dimensional major white matter bundle segmentation using diffusion magnetic resonance imaging |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256641/ https://www.ncbi.nlm.nih.gov/pubmed/37017902 http://dx.doi.org/10.1007/s12565-023-00715-9 |
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