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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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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. |
format | Online Article Text |
id | pubmed-7652839 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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