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

Autor: 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
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Scientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-021-00161-5
Popis: 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
Databáze: Directory of Open Access Journals
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