Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)

Abstract In this paper, we present an original multiple atlases level set framework (MALSF) for automatic, accurate and robust thalamus segmentation in magnetic resonance images (MRI). The contributions of the MALSF method are twofold. First, the main technical contribution is a novel label fusion s...

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Autores principales:	Minghui Zhang, Zhentai Lu, Qianjin Feng, Yu Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/08dcd9bf2dba4f949fe3233d2c39b03c
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spelling	oai:doaj.org-article:08dcd9bf2dba4f949fe3233d2c39b03c2021-12-02T12:32:39ZAutomatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)10.1038/s41598-017-04276-62045-2322https://doaj.org/article/08dcd9bf2dba4f949fe3233d2c39b03c2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04276-6https://doaj.org/toc/2045-2322Abstract In this paper, we present an original multiple atlases level set framework (MALSF) for automatic, accurate and robust thalamus segmentation in magnetic resonance images (MRI). The contributions of the MALSF method are twofold. First, the main technical contribution is a novel label fusion strategy in the level set framework. Label fusion is achieved by seeking an optimal level set function that minimizes energy functional with three terms: label fusion term, image based term, and regularization term. This strategy integrates shape prior, image information and the regularity of the thalamus. Second, we use propagated labels from multiple registration methods with different parameters to take full advantage of the complementary information of different registration methods. Since different registration methods and different atlases can yield complementary information, multiple registration and multiple atlases can be incorporated into the level set framework to improve the segmentation performance. Experiments have shown that the MALSF method can improve the segmentation accuracy for the thalamus. Compared to ground truth segmentation, the mean Dice metrics of our method are 0.9239 and 0.9200 for left and right thalamus.Minghui ZhangZhentai LuQianjin FengYu ZhangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Minghui Zhang Zhentai Lu Qianjin Feng Yu Zhang Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
description	Abstract In this paper, we present an original multiple atlases level set framework (MALSF) for automatic, accurate and robust thalamus segmentation in magnetic resonance images (MRI). The contributions of the MALSF method are twofold. First, the main technical contribution is a novel label fusion strategy in the level set framework. Label fusion is achieved by seeking an optimal level set function that minimizes energy functional with three terms: label fusion term, image based term, and regularization term. This strategy integrates shape prior, image information and the regularity of the thalamus. Second, we use propagated labels from multiple registration methods with different parameters to take full advantage of the complementary information of different registration methods. Since different registration methods and different atlases can yield complementary information, multiple registration and multiple atlases can be incorporated into the level set framework to improve the segmentation performance. Experiments have shown that the MALSF method can improve the segmentation accuracy for the thalamus. Compared to ground truth segmentation, the mean Dice metrics of our method are 0.9239 and 0.9200 for left and right thalamus.
format	article
author	Minghui Zhang Zhentai Lu Qianjin Feng Yu Zhang
author_facet	Minghui Zhang Zhentai Lu Qianjin Feng Yu Zhang
author_sort	Minghui Zhang
title	Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
title_short	Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
title_full	Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
title_fullStr	Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
title_full_unstemmed	Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)
title_sort	automatic thalamus segmentation from magnetic resonance images using multiple atlases level set framework (malsf)
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/08dcd9bf2dba4f949fe3233d2c39b03c
work_keys_str_mv	AT minghuizhang automaticthalamussegmentationfrommagneticresonanceimagesusingmultipleatlaseslevelsetframeworkmalsf AT zhentailu automaticthalamussegmentationfrommagneticresonanceimagesusingmultipleatlaseslevelsetframeworkmalsf AT qianjinfeng automaticthalamussegmentationfrommagneticresonanceimagesusingmultipleatlaseslevelsetframeworkmalsf AT yuzhang automaticthalamussegmentationfrommagneticresonanceimagesusingmultipleatlaseslevelsetframeworkmalsf
_version_	1718393959408992256

Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)

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