Abstract
Medical images of coronary artery plaque are always accompanied by the situation of extreme class imbalance. The traditional two-step methods locate the region of interest (ROI) in the sample firstly, and then segment the sample within the ROI. On the other hand, the traditional resampling methods use resampling strategies to increase the number of minority class samples to mitigate the effects of class imbalance. These two types of methods either make the network structure more complex or decrease training efficiency and performance of the model due to the increase of samples. This paper proposes a method including a novel focal weighted accuracy loss function and improved metrics evaluation algorithms to address the issues in the segmentation of coronary artery calcification plaque mentioned above. Experimental results on the selected dataset show the proposed method increased the training speed and improved the segmentation performance of the model without performing resampling on the dataset. Specifically, the F1-score was 0.873 5, the precision was 0.929 6, and the recall was 0.823 8. The F1-score was largely improved compared with the method using focal loss function. Furthermore, compared with methods with multiple models and methods via resampling the minority class samples, research results demonstrate that the proposed method improved the accuracy and efficiency in coronary artery plaque segmentation while has a shorter training time, which lays the foundation for improving the efficiency and scientific nature of diagnosing related diseases in the future.