Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography

Abstract 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 (D...

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Autores principales:	Jiyeon Ha, Taeyong Park, Hong-Kyu Kim, Youngbin Shin, Yousun Ko, Dong Wook Kim, Yu Sub Sung, Jiwoo Lee, Su Jung Ham, Seungwoo Khang, Heeryeol Jeong, Kyoyeong Koo, Jeongjin Lee, Kyung Won Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ca3c1fc0916449b9470c19b4f93fb3e
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spelling	oai:doaj.org-article:5ca3c1fc0916449b9470c19b4f93fb3e2021-11-08T10:50:51ZDevelopment of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography10.1038/s41598-021-00161-52045-2322https://doaj.org/article/5ca3c1fc0916449b9470c19b4f93fb3e2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-00161-5https://doaj.org/toc/2045-2322Abstract 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 cm2. 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.Jiyeon HaTaeyong ParkHong-Kyu KimYoungbin ShinYousun KoDong Wook KimYu Sub SungJiwoo LeeSu Jung HamSeungwoo KhangHeeryeol JeongKyoyeong KooJeongjin LeeKyung Won KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jiyeon Ha Taeyong Park Hong-Kyu Kim Youngbin Shin Yousun Ko Dong Wook Kim Yu Sub Sung Jiwoo Lee Su Jung Ham Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Kyung Won Kim Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography
description	Abstract 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 cm2. 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	article
author	Jiyeon Ha Taeyong Park Hong-Kyu Kim Youngbin Shin Yousun Ko Dong Wook Kim Yu Sub Sung Jiwoo Lee Su Jung Ham Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Kyung Won Kim
author_facet	Jiyeon Ha Taeyong Park Hong-Kyu Kim Youngbin Shin Yousun Ko Dong Wook Kim Yu Sub Sung Jiwoo Lee Su Jung Ham Seungwoo Khang Heeryeol Jeong Kyoyeong Koo Jeongjin Lee Kyung Won Kim
author_sort	Jiyeon Ha
title	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_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_sort	development of a fully automatic deep learning system for l3 selection and body composition assessment on computed tomography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5ca3c1fc0916449b9470c19b4f93fb3e
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Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography

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