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X-ray 영상에서 VHS와 콥 각도 자동 추출을 위한 흉추 분할 기법
이예은 ( Ye-eun Lee ),한승화 ( Seung-hwa Han ),이동규 ( Dong-gyu Lee ),김호준 ( Ho-joon Kim ) 한국정보처리학회 2023 정보처리학회논문지. 소프트웨어 및 데이터 공학 Vol.12 No.1
In this paper, we propose an organ segmentation technique for the automatic extraction of medical diagnostic indicators from X-ray images. In order to calculate diagnostic indicators of heart disease and spinal disease such as VHS(vertebral heart scale) and Cobb angle, it is necessary to accurately segment the thoracic spine, carina, and heart in a chest X-ray image. A deep neural network model in which the high-resolution representation of the image for each layer and the structure converted into a low-resolution feature map are connected in parallel was adopted. This structure enables the relative position information in the image to be effectively reflected in the segmentation process. It is shown that learning performance can be improved by combining the OCR module, in which pixel information and object information are mutually interacted in a multi-step process, and the channel attention module, which allows each channel of the network to be reflected as different weight values. In addition, a method of augmenting learning data is presented in order to provide robust performance against changes in the position, shape, and size of the subject in the X-ray image. The effectiveness of the proposed theory was evaluated through an experiment using 145 human chest X-ray images and 118 animal X-ray images.
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