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Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography
As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to se...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568923/ https://www.ncbi.nlm.nih.gov/pubmed/34737340 http://dx.doi.org/10.1038/s41598-021-00161-5 |
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author | Ha, Jiyeon Park, Taeyong Kim, Hong-Kyu Shin, Youngbin Ko, Yousun Kim, Dong Wook Sung, Yu Sub Lee, Jiwoo Ham, Su Jung Khang, Seungwoo Jeong, Heeryeol Koo, Kyoyeong Lee, Jeongjin Kim, Kyung Won |
author_facet | Ha, Jiyeon Park, Taeyong Kim, Hong-Kyu Shin, Youngbin Ko, Yousun Kim, Dong Wook Sung, Yu Sub Lee, Jiwoo Ham, Su Jung Khang, Seungwoo Jeong, Heeryeol Koo, Kyoyeong Lee, Jeongjin Kim, Kyung Won |
author_sort | Ha, Jiyeon |
collection | PubMed |
description | As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38–3.10 cm(2). A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas. |
format | Online Article Text |
id | pubmed-8568923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85689232021-11-05 Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography Ha, Jiyeon Park, Taeyong Kim, Hong-Kyu Shin, Youngbin Ko, Yousun Kim, Dong Wook Sung, Yu Sub Lee, Jiwoo Ham, Su Jung Khang, Seungwoo Jeong, Heeryeol Koo, Kyoyeong Lee, Jeongjin Kim, Kyung Won Sci Rep Article As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38–3.10 cm(2). A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas. Nature Publishing Group UK 2021-11-04 /pmc/articles/PMC8568923/ /pubmed/34737340 http://dx.doi.org/10.1038/s41598-021-00161-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Ha, Jiyeon Park, Taeyong Kim, Hong-Kyu Shin, Youngbin Ko, Yousun Kim, Dong Wook Sung, Yu Sub Lee, Jiwoo Ham, Su Jung Khang, Seungwoo Jeong, Heeryeol Koo, Kyoyeong Lee, Jeongjin Kim, Kyung Won Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title_full | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title_fullStr | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title_full_unstemmed | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title_short | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography |
title_sort | development of a fully automatic deep learning system for l3 selection and body composition assessment on computed tomography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8568923/ https://www.ncbi.nlm.nih.gov/pubmed/34737340 http://dx.doi.org/10.1038/s41598-021-00161-5 |
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