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Glass-cutting medical images via a mechanical image segmentation method based on crack propagation

Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segme...

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Detalles Bibliográficos
Autores principales: Huang, Yaqi, Hu, Ge, Ji, Changjin, Xiong, Huahui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652839/
https://www.ncbi.nlm.nih.gov/pubmed/33168802
http://dx.doi.org/10.1038/s41467-020-19392-7
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author Huang, Yaqi
Hu, Ge
Ji, Changjin
Xiong, Huahui
author_facet Huang, Yaqi
Hu, Ge
Ji, Changjin
Xiong, Huahui
author_sort Huang, Yaqi
collection PubMed
description Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential.
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spelling pubmed-76528392020-11-12 Glass-cutting medical images via a mechanical image segmentation method based on crack propagation Huang, Yaqi Hu, Ge Ji, Changjin Xiong, Huahui Nat Commun Article Medical image segmentation is crucial in diagnosing and treating diseases, but automatic segmentation of complex images is very challenging. Here we present a method, called the crack propagation method (CPM), based on the principles of fracture mechanics. This unique method converts the image segmentation problem into a mechanical one, extracting the boundary information of the target area by tracing the crack propagation on a thin plate with grooves corresponding to the area edge. The greatest advantage of CPM is in segmenting images involving blurred or even discontinuous boundaries, a task difficult to achieve by existing auto-segmentation methods. The segmentation results for synthesized images and real medical images show that CPM has high accuracy in segmenting complex boundaries. With increasing demand for medical imaging in clinical practice and research, this method will show its unique potential. Nature Publishing Group UK 2020-11-09 /pmc/articles/PMC7652839/ /pubmed/33168802 http://dx.doi.org/10.1038/s41467-020-19392-7 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Huang, Yaqi
Hu, Ge
Ji, Changjin
Xiong, Huahui
Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title_full Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title_fullStr Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title_full_unstemmed Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title_short Glass-cutting medical images via a mechanical image segmentation method based on crack propagation
title_sort glass-cutting medical images via a mechanical image segmentation method based on crack propagation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7652839/
https://www.ncbi.nlm.nih.gov/pubmed/33168802
http://dx.doi.org/10.1038/s41467-020-19392-7
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