Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI

Abstract The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the...

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Autores principales:	Sang-Heon Lim, Jihyun Yoon, Young Jae Kim, Chang-Ki Kang, Seo-Eun Cho, Kwang Gi Kim, Seung-Gul Kang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/919069e2d3a9483abc76bc85e81a5103
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spelling	oai:doaj.org-article:919069e2d3a9483abc76bc85e81a51032021-12-02T14:34:02ZReproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI10.1038/s41598-021-92952-z2045-2322https://doaj.org/article/919069e2d3a9483abc76bc85e81a51032021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92952-zhttps://doaj.org/toc/2045-2322Abstract The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the manual segmentation results vary greatly depending on the observer. Therefore, there is a great need for automatic quantitative analytic methods of the habenula for psychiatric research purposes. Here we propose an automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a deep learning-based semantic segmentation network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, recall, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intra-class correlation coefficient reached 0.870 in the volume estimation task. This study demonstrates that this deep learning-based method can provide accurate and quantitative analytic results of the habenula. By providing rapid and quantitative information on the habenula, we expect our proposed method will aid future psychiatric disease studies.Sang-Heon LimJihyun YoonYoung Jae KimChang-Ki KangSeo-Eun ChoKwang Gi KimSeung-Gul KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sang-Heon Lim Jihyun Yoon Young Jae Kim Chang-Ki Kang Seo-Eun Cho Kwang Gi Kim Seung-Gul Kang Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
description	Abstract The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the manual segmentation results vary greatly depending on the observer. Therefore, there is a great need for automatic quantitative analytic methods of the habenula for psychiatric research purposes. Here we propose an automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a deep learning-based semantic segmentation network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, recall, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intra-class correlation coefficient reached 0.870 in the volume estimation task. This study demonstrates that this deep learning-based method can provide accurate and quantitative analytic results of the habenula. By providing rapid and quantitative information on the habenula, we expect our proposed method will aid future psychiatric disease studies.
format	article
author	Sang-Heon Lim Jihyun Yoon Young Jae Kim Chang-Ki Kang Seo-Eun Cho Kwang Gi Kim Seung-Gul Kang
author_facet	Sang-Heon Lim Jihyun Yoon Young Jae Kim Chang-Ki Kang Seo-Eun Cho Kwang Gi Kim Seung-Gul Kang
author_sort	Sang-Heon Lim
title	Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
title_short	Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
title_full	Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
title_fullStr	Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
title_full_unstemmed	Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI
title_sort	reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 tesla mri
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/919069e2d3a9483abc76bc85e81a5103
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Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI

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