ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술대회 Automatic CAC Voxel Classification with Multi-scale CNN Architecture
Cited 2 time in scopus Download 2 time Share share facebook twitter linkedin kakaostory
김원식, 정호열, 최재훈
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1351-1353
19HS1500, 심혈관질환을 위한 인공지능 주치의 기술 개발, 김승환
Coronary Artery Calcification (CAC) score is one of the most important measures in determining the degree of cardiovascular disease. It is time-consuming to do this manually or semi-automatically, so automatic CAC scoring methods are being studied. Most methods classify the calcified pixels(2D) or voxels(2.5D or 3D) and calculate the CAC score. We present a new automatic CAC voxel classification model with multi-scale CNN architecture which can reflect the advantages of large receptive CNN and small receptive CNN. This study used a cardiac CT dataset of 98 patients from Asan Medical Center in South Korea. The dataset consisted of a cardiac CT DICOM raw data and a CAC label data annotated by a cardiac radiologist. A total of 10,000 voxels were selected for each calcified artery(LM, CX, LAD, RCA) and background(BG), so a total of 50,000 voxels were used in training and testing. Our proposed model showed an accuracy of 89.58% with cardiac CT dataset of Asan Medical Center. The performance of our model is generally better when compared to other automatic CAC classification models.
KSP 제안 키워드
Cardiovascular diseases(CVD), Classification models, Multi-scale, Proposed model, South Korea, Voxel classification, cardiac CT, coronary artery, raw data, scoring methods, training and testing