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http://dx.doi.org/10.3745/KTCCS.2022.11.7.233

Development of Cloud-Based Medical Image Labeling System and It's Quantitative Analysis of Sarcopenia  

Lee, Chung-Sub (원광대학교 의료융합연구센터)
Lim, Dong-Wook (원광대학교 의료융합연구센터)
Kim, Ji-Eon (원광대학교 의료융합연구센터)
Noh, Si-Hyeong (원광대학교 의료융합연구센터)
Yu, Yeong-Ju (세종사이버대학교 소프트웨어공학과)
Kim, Tae-Hoon (원광대학교병원 스마트사업팀)
Yoon, Kwon-Ha (성균관대학교 삼성창원병원 영상의학과)
Jeong, Chang-Won (원광대학교병원 스마트사업팀)
Publication Information
KIPS Transactions on Computer and Communication Systems / v.11, no.7, 2022 , pp. 233-240 More about this Journal
Abstract
Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.
Keywords
Artificial Intelligence; Medical Image; DICOM; CT; Labeling Data; Sarcopenia;
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Times Cited By KSCI : 1  (Citation Analysis)
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