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Deep Learning-Based Fully Automated Segmentation of Regional Muscle Volume and Spatial Intermuscular Fat Using CT

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收录情况： ◇ SCIE

作者：
Zhang Rui[1,3] ; He Aiting[2] ; Xia Wei[3] ; Su Yongbin[4] ; Jian Junming[3] ; Liu Yandong[4] ; Guo Zhe[4] ; Shi Wei[3] ; Zhang Zhenguang[5] ; He Bo[5] ; Cheng Xiaoguang[4] ; Gao Xin[3] ; Liu Yajun[6] ; Wang Ling[4] ;
机构： [1]School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China (R.Z.); Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (R.Z., W.X., J.J., W.S., X.G.). [2]Department of Radiology, Yuxi Third Hospital, Yuxi, China (A.H.). [3]Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (R.Z., W.X., J.J., W.S., X.G.). [4]Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China (Y.S., Y.L., Z.G., X.C., L.W.). [5]Department of Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China (Z.Z., B.H.). [6]Department of Spine Surgery, Beijing Jishuitan Hospital, Beijing, China (Y.L.). [7]Department of Radiology, Beijing Jishuitan Hospital, Beijing 100035, China (Y.S., Y.L., Z.G., X.C., L.W.). Electronic address: doctorwl@bjmu.edu.cn.
出处：
DOI： 10.1016/j.acra.2023.06.009
ISSN：

关键词： Deep learning Segmentation Sarcopenia Gluteus maximus muscle Fat fraction

摘要：
We aim to develop a CT-based deep learning (DL) system for fully automatic segmentation of regional muscle volume and measurement of the spatial intermuscular fat distribution of the gluteus maximus muscle.A total of 472 subjects were enrolled and randomly assigned to one of three groups: a training set, test set 1, and test set 2. For each subject in the training set and test set 1, we selected six slices of the CT images as the region of interest for manual segmentation by a radiologist. For each subject in test set 2, we selected all slices of the gluteus maximus muscle on the CT images for manual segmentation. The DL system was constructed using Attention U-Net and the Otsu binary thresholding method to segment the muscle and measure the fat fraction of the gluteus maximus muscle. The segmentation results of the DL system were evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and the average surface distance (ASD) as metrics. Intraclass correlation coefficients (ICCs) and Bland-Altman plots were used to assess agreement in the measurements of fat fraction between the radiologist and the DL system.The DL system showed good segmentation performance on the two test sets, with DSCs of 0.930 and 0.873, respectively. The fat fraction of the gluteus maximus muscle measured by the DL system was in agreement with the radiologist (ICC=0.748).The proposed DL system showed accurate, fully automated segmentation performance and good agreement with the radiologist at fat fraction evaluation, and can be further used for muscle evaluation.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

基金：
This work is supported in part by National Natural Science Foundation of China (Grant Nos. 81901718), the Applied Basic Research Project of Yunnan Province-Kunming Medical University Joint Fund (202001AY070001-200),Beijing Hospitals Authority Youth Programme (code: QML20200402), Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support (code: ZYLX202107), Natural Science Foundation of Shandong Province (No. ZR2022QF099), and National Natural Science Foundation of China (Grant Nos. 81971617)
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外文
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出版当年[2024]版：
无
最新[2023]版：
大类 | 2 区 医学
小类 | 2 区 核医学
JCR分区：
出版当年[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2023]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

影响因子： 最新[2023版] 最新五年平均 出版当年[2023版] 出版当年五年平均 出版前一年[2022版]

第一作者：
第一作者机构： [1]School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China (R.Z.); Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (R.Z., W.X., J.J., W.S., X.G.). [3]Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China (R.Z., W.X., J.J., W.S., X.G.).
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Zhang Rui,He Aiting,Xia Wei,et al.Deep Learning-Based Fully Automated Segmentation of Regional Muscle Volume and Spatial Intermuscular Fat Using CT[J].ACADEMIC RADIOLOGY.2023,30(10):2280-2289.doi:10.1016/j.acra.2023.06.009.
APA：
Zhang Rui,He Aiting,Xia Wei,Su Yongbin,Jian Junming...&Wang Ling.(2023).Deep Learning-Based Fully Automated Segmentation of Regional Muscle Volume and Spatial Intermuscular Fat Using CT.ACADEMIC RADIOLOGY,30,(10)
MLA：
Zhang Rui,et al."Deep Learning-Based Fully Automated Segmentation of Regional Muscle Volume and Spatial Intermuscular Fat Using CT".ACADEMIC RADIOLOGY 30..10(2023):2280-2289

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