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Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis

BACKGROUND: Sarcopenia is generally diagnosed by the total area of skeletal muscle in the CT axial slice located in the third lumbar (L3) vertebra. However, patients with severe liver cirrhosis cannot accurately obtain the corresponding total skeletal muscle because their abdominal muscles are squee...

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Autores principales: Song, Genshen, Zhou, Ji, Wang, Kang, Yao, Demin, Chen, Shiyao, Shi, Yonghong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289291/
https://www.ncbi.nlm.nih.gov/pubmed/37360174
http://dx.doi.org/10.3389/fnins.2023.1203823
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author Song, Genshen
Zhou, Ji
Wang, Kang
Yao, Demin
Chen, Shiyao
Shi, Yonghong
author_facet Song, Genshen
Zhou, Ji
Wang, Kang
Yao, Demin
Chen, Shiyao
Shi, Yonghong
author_sort Song, Genshen
collection PubMed
description BACKGROUND: Sarcopenia is generally diagnosed by the total area of skeletal muscle in the CT axial slice located in the third lumbar (L3) vertebra. However, patients with severe liver cirrhosis cannot accurately obtain the corresponding total skeletal muscle because their abdominal muscles are squeezed, which affects the diagnosis of sarcopenia. PURPOSE: This study proposes a novel lumbar skeletal muscle network to automatically segment multi-regional skeletal muscle from CT images, and explores the relationship between cirrhotic sarcopenia and each skeletal muscle region. METHODS: This study utilizes the skeletal muscle characteristics of different spatial regions to improve the 2.5D U-Net enhanced by residual structure. Specifically, a 3D texture attention enhancement block is proposed to tackle the issue of blurred edges with similar intensities and poor segmentation between different skeletal muscle regions, which contains skeletal muscle shape and muscle fibre texture to spatially constrain the integrity of skeletal muscle region and alleviate the difficulty of identifying muscle boundaries in axial slices. Subsequentially, a 3D encoding branch is constructed in conjunction with a 2.5D U-Net, which segments the lumbar skeletal muscle in multiple L3-related axial CT slices into four regions. Furthermore, the diagnostic cut-off values of the L3 skeletal muscle index (L3SMI) are investigated for identifying cirrhotic sarcopenia in four muscle regions segmented from CT images of 98 patients with liver cirrhosis. RESULTS: Our method is evaluated on 317 CT images using the five-fold cross-validation method. For the four skeletal muscle regions segmented in the images from the independent test set, the avg. DSC is 0.937 and the avg. surface distance is 0.558 mm. For sarcopenia diagnosis in 98 patients with liver cirrhosis, the cut-off values of Rectus Abdominis, Right Psoas, Left Psoas, and Paravertebral are 16.67, 4.14, 3.76, and 13.20 cm(2)/m(2) in females, and 22.51, 5.84, 6.10, and 17.28 cm(2)/m(2) in males, respectively. CONCLUSION: The proposed method can segment four skeletal muscle regions related to the L3 vertebra with high accuracy. Furthermore, the analysis shows that the Rectus Abdominis region can be used to assist in the diagnosis of sarcopenia when the total muscle is not available.
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spelling pubmed-102892912023-06-24 Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis Song, Genshen Zhou, Ji Wang, Kang Yao, Demin Chen, Shiyao Shi, Yonghong Front Neurosci Neuroscience BACKGROUND: Sarcopenia is generally diagnosed by the total area of skeletal muscle in the CT axial slice located in the third lumbar (L3) vertebra. However, patients with severe liver cirrhosis cannot accurately obtain the corresponding total skeletal muscle because their abdominal muscles are squeezed, which affects the diagnosis of sarcopenia. PURPOSE: This study proposes a novel lumbar skeletal muscle network to automatically segment multi-regional skeletal muscle from CT images, and explores the relationship between cirrhotic sarcopenia and each skeletal muscle region. METHODS: This study utilizes the skeletal muscle characteristics of different spatial regions to improve the 2.5D U-Net enhanced by residual structure. Specifically, a 3D texture attention enhancement block is proposed to tackle the issue of blurred edges with similar intensities and poor segmentation between different skeletal muscle regions, which contains skeletal muscle shape and muscle fibre texture to spatially constrain the integrity of skeletal muscle region and alleviate the difficulty of identifying muscle boundaries in axial slices. Subsequentially, a 3D encoding branch is constructed in conjunction with a 2.5D U-Net, which segments the lumbar skeletal muscle in multiple L3-related axial CT slices into four regions. Furthermore, the diagnostic cut-off values of the L3 skeletal muscle index (L3SMI) are investigated for identifying cirrhotic sarcopenia in four muscle regions segmented from CT images of 98 patients with liver cirrhosis. RESULTS: Our method is evaluated on 317 CT images using the five-fold cross-validation method. For the four skeletal muscle regions segmented in the images from the independent test set, the avg. DSC is 0.937 and the avg. surface distance is 0.558 mm. For sarcopenia diagnosis in 98 patients with liver cirrhosis, the cut-off values of Rectus Abdominis, Right Psoas, Left Psoas, and Paravertebral are 16.67, 4.14, 3.76, and 13.20 cm(2)/m(2) in females, and 22.51, 5.84, 6.10, and 17.28 cm(2)/m(2) in males, respectively. CONCLUSION: The proposed method can segment four skeletal muscle regions related to the L3 vertebra with high accuracy. Furthermore, the analysis shows that the Rectus Abdominis region can be used to assist in the diagnosis of sarcopenia when the total muscle is not available. Frontiers Media S.A. 2023-06-05 /pmc/articles/PMC10289291/ /pubmed/37360174 http://dx.doi.org/10.3389/fnins.2023.1203823 Text en Copyright © 2023 Song, Zhou, Wang, Yao, Chen and Shi. 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 Neuroscience
Song, Genshen
Zhou, Ji
Wang, Kang
Yao, Demin
Chen, Shiyao
Shi, Yonghong
Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title_full Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title_fullStr Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title_full_unstemmed Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title_short Segmentation of multi-regional skeletal muscle in abdominal CT image for cirrhotic sarcopenia diagnosis
title_sort segmentation of multi-regional skeletal muscle in abdominal ct image for cirrhotic sarcopenia diagnosis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289291/
https://www.ncbi.nlm.nih.gov/pubmed/37360174
http://dx.doi.org/10.3389/fnins.2023.1203823
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