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Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, int...

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Autores principales: Le, Doan Cong, Chinnasarn, Krisana, Chansangrat, Jirapa, Keeratibharat, Nattawut, Horkaew, Paramate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969941/
https://www.ncbi.nlm.nih.gov/pubmed/33731736
http://dx.doi.org/10.1038/s41598-021-85436-7
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author Le, Doan Cong
Chinnasarn, Krisana
Chansangrat, Jirapa
Keeratibharat, Nattawut
Horkaew, Paramate
author_facet Le, Doan Cong
Chinnasarn, Krisana
Chansangrat, Jirapa
Keeratibharat, Nattawut
Horkaew, Paramate
author_sort Le, Doan Cong
collection PubMed
description Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation.
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spelling pubmed-79699412021-03-19 Semi-automatic liver segmentation based on probabilistic models and anatomical constraints Le, Doan Cong Chinnasarn, Krisana Chansangrat, Jirapa Keeratibharat, Nattawut Horkaew, Paramate Sci Rep Article Segmenting a liver and its peripherals from abdominal computed tomography is a crucial step toward computer aided diagnosis and therapeutic intervention. Despite the recent advances in computing methods, faithfully segmenting the liver has remained a challenging task, due to indefinite boundary, intensity inhomogeneity, and anatomical variations across subjects. In this paper, a semi-automatic segmentation method based on multivariable normal distribution of liver tissues and graph-cut sub-division is presented. Although it is not fully automated, the method minimally involves human interactions. Specifically, it consists of three main stages. Firstly, a subject specific probabilistic model was built from an interior patch, surrounding a seed point specified by the user. Secondly, an iterative assignment of pixel labels was applied to gradually update the probabilistic map of the tissues based on spatio-contextual information. Finally, the graph-cut model was optimized to extract the 3D liver from the image. During post-processing, overly segmented nodal regions due to fuzzy tissue separation were removed, maintaining its correct anatomy by using robust bottleneck detection with adjacent contour constraint. The proposed system was implemented and validated on the MICCAI SLIVER07 dataset. The experimental results were benchmarked against the state-of-the-art methods, based on major clinically relevant metrics. Both visual and numerical assessments reported herein indicated that the proposed system could improve the accuracy and reliability of asymptomatic liver segmentation. Nature Publishing Group UK 2021-03-17 /pmc/articles/PMC7969941/ /pubmed/33731736 http://dx.doi.org/10.1038/s41598-021-85436-7 Text en © The Author(s) 2021 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/.
spellingShingle Article
Le, Doan Cong
Chinnasarn, Krisana
Chansangrat, Jirapa
Keeratibharat, Nattawut
Horkaew, Paramate
Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title_full Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title_fullStr Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title_full_unstemmed Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title_short Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
title_sort semi-automatic liver segmentation based on probabilistic models and anatomical constraints
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969941/
https://www.ncbi.nlm.nih.gov/pubmed/33731736
http://dx.doi.org/10.1038/s41598-021-85436-7
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