An automatic multi-class coronary atherosclerosis plaque detection and classification framework

• Published in: Medical & biological engineering & computing Vol. 57; no. 1; pp. 245 - 257
• Main Authors: Zhao, Fengjun, Wu, Bin, Chen, Fei, Cao, Xin, Yi, Huangjian, Hou, Yuqing, He, Xiaowei, Liang, Jimin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2019; Springer Nature B.V
• Subjects: Angiography; Arteries; Arteriosclerosis; Atherosclerosis; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Calcification; Classification; Classifiers; Computed tomography; Computer Applications; Coronary artery; Cross-sections; Diagnostic systems; Feature extraction; Human Physiology; Imaging; Original Article; Plaques; Radiology; Segmentation; Support vector machines; Computed tomography angiography; Coronary atherosclerosis plaque; Detection; Classification
• ISSN: 0140-0118; 1741-0444
• DOI: 10.1007/s11517-018-1880-6

Detection of different classes of atherosclerotic plaques is important for early intervention of coronary artery diseases. However, previous methods focused either on the detection of a specific class of coronary plaques or on the distinction between plaques and normal arteries, neglecting the classification of different classes of plaques. Therefore, we proposed an automatic multi-class coronary atherosclerosis plaque detection and classification framework. Firstly, we retrieved the transverse cross sections along centerlines from the computed tomography angiography. Secondly, we extracted the region of interests based on coarse segmentation. Thirdly, we extracted a random radius symmetry (RRS) feature vector, which incorporates multiple descriptions into a random strategy and greatly augments the training data. Finally, we fed the RRS feature vector into the multi-class coronary plaque classifier. In experiments, we compared our proposed framework with other methods on the cross sections of Rotterdam Coronary Datasets, including 729 non-calcified plaques, 511 calcified plaques, and 546 mixed plaques. Our RRS with support vector machine outperforms the intensity feature vector and the random forest classifier, with the average precision of 92.6 ± 1.9% and average recall of 94.3 ± 2.1%. The proposed framework provides a computer-aided diagnostic method for multi-class plaque detection and classification. Graphical abstract Diagram of the proposed automatic multi-class coronary atherosclerosis plaque detection and classification framework. ᅟ