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Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling
Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-mea...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637091/ https://www.ncbi.nlm.nih.gov/pubmed/36335227 http://dx.doi.org/10.1038/s41598-022-23408-1 |
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author | El-Melegy, Moumen Kamel, Rasha El-Ghar, Mohamed Abou Shehata, Mohamed Khalifa, Fahmi El-Baz, Ayman |
author_facet | El-Melegy, Moumen Kamel, Rasha El-Ghar, Mohamed Abou Shehata, Mohamed Khalifa, Fahmi El-Baz, Ayman |
author_sort | El-Melegy, Moumen |
collection | PubMed |
description | Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels. |
format | Online Article Text |
id | pubmed-9637091 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96370912022-11-07 Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling El-Melegy, Moumen Kamel, Rasha El-Ghar, Mohamed Abou Shehata, Mohamed Khalifa, Fahmi El-Baz, Ayman Sci Rep Article Early diagnosis of transplanted kidney function requires precise Kidney segmentation from Dynamic Contrast-Enhanced Magnetic Resonance Imaging images as a preliminary step. In this regard, this paper aims to propose an automated and accurate DCE-MRI kidney segmentation method integrating fuzzy c-means (FCM) clustering and Markov random field modeling into a level set formulation. The fuzzy memberships, kidney’s shape prior model, and spatial interactions modeled using a second-order MRF guide the LS contour evolution towards the target kidney. Several experiments on real medical data of 45 subjects have shown that the proposed method can achieve high and consistent segmentation accuracy regardless of where the LS contour was initialized. It achieves an accuracy of 0.956 ± 0.019 in Dice similarity coefficient (DSC) and 1.15 ± 1.46 in 95% percentile of Hausdorff distance (HD95). Our quantitative comparisons confirm the superiority of the proposed method over several LS methods with an average improvement of more than 0.63 in terms of HD95. It also offers HD95 improvements of 9.62 and 3.94 over two deep neural networks based on the U-Net model. The accuracy improvements are experimentally found to be more profound on low-contrast images as well as DCE-MRI images with high noise levels. Nature Publishing Group UK 2022-11-05 /pmc/articles/PMC9637091/ /pubmed/36335227 http://dx.doi.org/10.1038/s41598-022-23408-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 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 | Article El-Melegy, Moumen Kamel, Rasha El-Ghar, Mohamed Abou Shehata, Mohamed Khalifa, Fahmi El-Baz, Ayman Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title | Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title_full | Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title_fullStr | Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title_full_unstemmed | Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title_short | Kidney segmentation from DCE-MRI converging level set methods, fuzzy clustering and Markov random field modeling |
title_sort | kidney segmentation from dce-mri converging level set methods, fuzzy clustering and markov random field modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9637091/ https://www.ncbi.nlm.nih.gov/pubmed/36335227 http://dx.doi.org/10.1038/s41598-022-23408-1 |
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