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Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net

BACKGROUND: White matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade...

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Autores principales: Gong, Tan, Han, Hualu, Tan, Zheng, Ning, Zihan, Qiao, Huiyu, Yu, Miaoxin, Zhao, Xihai, Tang, Xiaoying, Liu, Gaifen, Shang, Fei, Liu, Shuai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713228/
https://www.ncbi.nlm.nih.gov/pubmed/36468062
http://dx.doi.org/10.3389/fneur.2022.1021477
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author Gong, Tan
Han, Hualu
Tan, Zheng
Ning, Zihan
Qiao, Huiyu
Yu, Miaoxin
Zhao, Xihai
Tang, Xiaoying
Liu, Gaifen
Shang, Fei
Liu, Shuai
author_facet Gong, Tan
Han, Hualu
Tan, Zheng
Ning, Zihan
Qiao, Huiyu
Yu, Miaoxin
Zhao, Xihai
Tang, Xiaoying
Liu, Gaifen
Shang, Fei
Liu, Shuai
author_sort Gong, Tan
collection PubMed
description BACKGROUND: White matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade U) for the segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images. METHODS: A total of 253 subjects were recruited in the present study. All subjects underwent 2D T2-FLAIR scan on a 3.0 Tesla MR scanner. Both contours of pvWMHs and dWMHs were manually delineated by the observers and considered as the gold standard. Fazekas scale was used to evaluate the burdens of pvWMHs and dWMHs, respectively. Cascade U consisted of a segmentation U-net and a differentiation U-net and was trained with a combined loss function. The performance of Cascade U was compared with two other U-net models (Pipeline U and Separate U). Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), precision, and recall were used to evaluate the performances of all models. The linear correlations between WMHs volume (WMHV) measured by all models and the gold standard were also conducted. RESULTS: Compared with other models, Cascade U exhibited a better performance on WMHs segmentation and pvWMHs identification. Cascade U achieved DSC values of 0.605 ± 0.135, 0.517 ± 0.263, and 0.510 ± 0.241 and MCC values of 0.617 ± 0.122, 0.526 ± 0.263, and 0.522 ± 0.243 on the segmentation of total WMHs, pvWMHs, and dWMHs, respectively. Cascade U exhibited strong correlations with the gold standard on measuring WMHV (R(2) = 0.954, p < 0.001), pvWMHV (R(2) = 0.933, p < 0.001), and dWMHV (R(2) = 0.918, p < 0.001). A significant correlation was found on lesion volume between Cascade U and gold standard (r > 0.510, p < 0.001). CONCLUSION: Cascade U showed competitive results in segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images, indicating potential feasibility in precisely evaluating the burdens of WMHs.
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spelling pubmed-97132282022-12-02 Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net Gong, Tan Han, Hualu Tan, Zheng Ning, Zihan Qiao, Huiyu Yu, Miaoxin Zhao, Xihai Tang, Xiaoying Liu, Gaifen Shang, Fei Liu, Shuai Front Neurol Neurology BACKGROUND: White matter hyperintensities (WMHs) are a subtype of cerebral small vessel disease and can be divided into periventricular WMHs (pvWMHs) and deep WMHs (dWMHs). pvWMHs and dWMHs were proved to be determined by different etiologies. This study aimed to develop a 2D Cascade U-net (Cascade U) for the segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images. METHODS: A total of 253 subjects were recruited in the present study. All subjects underwent 2D T2-FLAIR scan on a 3.0 Tesla MR scanner. Both contours of pvWMHs and dWMHs were manually delineated by the observers and considered as the gold standard. Fazekas scale was used to evaluate the burdens of pvWMHs and dWMHs, respectively. Cascade U consisted of a segmentation U-net and a differentiation U-net and was trained with a combined loss function. The performance of Cascade U was compared with two other U-net models (Pipeline U and Separate U). Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), precision, and recall were used to evaluate the performances of all models. The linear correlations between WMHs volume (WMHV) measured by all models and the gold standard were also conducted. RESULTS: Compared with other models, Cascade U exhibited a better performance on WMHs segmentation and pvWMHs identification. Cascade U achieved DSC values of 0.605 ± 0.135, 0.517 ± 0.263, and 0.510 ± 0.241 and MCC values of 0.617 ± 0.122, 0.526 ± 0.263, and 0.522 ± 0.243 on the segmentation of total WMHs, pvWMHs, and dWMHs, respectively. Cascade U exhibited strong correlations with the gold standard on measuring WMHV (R(2) = 0.954, p < 0.001), pvWMHV (R(2) = 0.933, p < 0.001), and dWMHV (R(2) = 0.918, p < 0.001). A significant correlation was found on lesion volume between Cascade U and gold standard (r > 0.510, p < 0.001). CONCLUSION: Cascade U showed competitive results in segmentation and differentiation of pvWMHs and dWMHs on 2D T2-FLAIR images, indicating potential feasibility in precisely evaluating the burdens of WMHs. Frontiers Media S.A. 2022-11-17 /pmc/articles/PMC9713228/ /pubmed/36468062 http://dx.doi.org/10.3389/fneur.2022.1021477 Text en Copyright © 2022 Gong, Han, Tan, Ning, Qiao, Yu, Zhao, Tang, Liu, Shang and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neurology
Gong, Tan
Han, Hualu
Tan, Zheng
Ning, Zihan
Qiao, Huiyu
Yu, Miaoxin
Zhao, Xihai
Tang, Xiaoying
Liu, Gaifen
Shang, Fei
Liu, Shuai
Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title_full Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title_fullStr Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title_full_unstemmed Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title_short Segmentation and differentiation of periventricular and deep white matter hyperintensities in 2D T2-FLAIR MRI based on a cascade U-net
title_sort segmentation and differentiation of periventricular and deep white matter hyperintensities in 2d t2-flair mri based on a cascade u-net
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9713228/
https://www.ncbi.nlm.nih.gov/pubmed/36468062
http://dx.doi.org/10.3389/fneur.2022.1021477
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