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

Abstract 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 boun...

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Autores principales:	Doan Cong Le, Krisana Chinnasarn, Jirapa Chansangrat, Nattawut Keeratibharat, Paramate Horkaew
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4b391c98b3e840d3bffc514e28750de1
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spelling	oai:doaj.org-article:4b391c98b3e840d3bffc514e28750de12021-12-02T16:30:37ZSemi-automatic liver segmentation based on probabilistic models and anatomical constraints10.1038/s41598-021-85436-72045-2322https://doaj.org/article/4b391c98b3e840d3bffc514e28750de12021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85436-7https://doaj.org/toc/2045-2322Abstract 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.Doan Cong LeKrisana ChinnasarnJirapa ChansangratNattawut KeeratibharatParamate HorkaewNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Doan Cong Le Krisana Chinnasarn Jirapa Chansangrat Nattawut Keeratibharat Paramate Horkaew Semi-automatic liver segmentation based on probabilistic models and anatomical constraints
description	Abstract 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.
format	article
author	Doan Cong Le Krisana Chinnasarn Jirapa Chansangrat Nattawut Keeratibharat Paramate Horkaew
author_facet	Doan Cong Le Krisana Chinnasarn Jirapa Chansangrat Nattawut Keeratibharat Paramate Horkaew
author_sort	Doan Cong Le
title	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_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_sort	semi-automatic liver segmentation based on probabilistic models and anatomical constraints
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4b391c98b3e840d3bffc514e28750de1
work_keys_str_mv	AT doancongle semiautomaticliversegmentationbasedonprobabilisticmodelsandanatomicalconstraints AT krisanachinnasarn semiautomaticliversegmentationbasedonprobabilisticmodelsandanatomicalconstraints AT jirapachansangrat semiautomaticliversegmentationbasedonprobabilisticmodelsandanatomicalconstraints AT nattawutkeeratibharat semiautomaticliversegmentationbasedonprobabilisticmodelsandanatomicalconstraints AT paramatehorkaew semiautomaticliversegmentationbasedonprobabilisticmodelsandanatomicalconstraints
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Semi-automatic liver segmentation based on probabilistic models and anatomical constraints

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