기계학습을 통한 복부 CT영상에서 요로결석 분할 모델및 AI 웹 애플리케이션 개발
Urinary Stones Segmentation Model and AI Web Application Development in Abdominal CT Images Through Machine Learning
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초록
의료분야 인공지능 기술이 분석과 알고리즘 개발에 중점을 두었으나 점차 제품으로 서비스하기 위한 Web 애플리케이션 개발로 변화되고 있다. 본 연구는 복부 CT 영상에서 요로결석(Urinary Stone) 분할모델과 이를 기반으로 한 인공지능 웹 애플리케이션에 대해 기술한다. 이를 구현하기위해 의료영상 분야에서 이미지 분할을 목적으로 제안된 End-to-End 방식의 Fully-Convolutional Network 기반 모델인 U-Net을 사용하여 모델을개발하였다. 그리고 Python 기반의 Flask라는 마이크로 웹 프레임워크를 사용하여 AWS 클라우드 기반 웹 애플리케이션으로 개발하였다. 끝으로모델 서빙으로 요로결석 분할모델이 예측한 결과를 인공지능 웹 애플리케이션 서비스 수행 결과로 보인다. 제안한 AI 웹 애플리케이션 서비스가선별 검사에 활용되기를 기대한다.
Abstract
Artificial intelligence technology in the medical field initially focused on analysis and algorithm development, but it is gradually changingto web application development for service as a product. This paper describes a Urinary Stone segmentation model in abdominal CTimages and an artificial intelligence web application based on it. To implement this, a model was developed using U-Net, a fullyconvolutionalnetwork-based model of the end-to-end method proposed for the purpose of image segmentation in the medical imagingfield. And for web service development, it was developed based on AWS cloud using a Python-based micro web framework called Flask. Finally, the result predicted by the urolithiasis segmentation model by model serving is shown as the result of performing the AI webapplication service. We expect that our proposed AI web application service will be utilized for screening test.
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