ESA-UNet for assisted diagnosis of cardiac magnetic resonance image based on the semantic segmentation of the heart - Li, Yuanzhe; Liu, Zhiqiang; Lai, Qingquan; Li, Shuting; Guo, Yifan; Wang, Yi; Dai, Zhangsheng; Huang, Jing (Frontiers in Cardiovascular Medicine, 2022)
Background: Cardiovascular diseases have become the number one disease affecting human health in today's society. In the diagnosis of cardiac diseases, Magnetic resonance image (MRI) technology is the most widely used one. However, in clinical diagnosis, the analysis of MRI relies on manual work, which is laborious and time-consuming, and also easily influenced by the subjective experience of doctors. Methods: In this paper, we propose an artificial intelligence-aided diagnosis system for cardiac MRI with image segmentation as the main component to assist in the diagnosis of cardiovascular diseases. We first performed adequate preprocessing of MRI. The preprocessing steps include detection of regions of interest of cardiac MRI data, as well as data normalization and data enhancement, and then we input the images after data preprocessing into the deep learning network module of ESA-Unet for identification of the aorta in order to obtain preliminary segmentation results, and finally, the boundaries of the segmentation results are further optimized using conditional random fields. For ROI detection, we first use standard deviation filters for filtering to find regions in the heart cycle image sequence where pixel intensity varies strongly with time and then use Canny edge detection and Hough transform techniques to find the region of interest containing the heart. The ESA-Unet proposed in this paper, on the other hand, is jointly designed with a self-attentive mechanism and multi-scale jump connection based on convolutional networks. Results: The experimental dataset used in this paper is from the Department of Cardiology at The Affiliated Hospital of Qingdao University. Experiments compare other convolution-based methods such as UNet, FCN, FPN, and PSPNet, and the results show that our model achieves the best results on Acc, Pr, ReCall, DSC, and IoU metrics. After comparative analysis, the experimental results show that the ESA-UNet network segmentation model designed in this paper has higher accuracy, intuitiveness, and more application value than traditional image segmentation algorithms. Conclusion: With the continuous application of nuclear magnetic resonance technology in clinical diagnosis, the method in this paper is expected to become a tool that can effectively improve the efficiency of doctors' diagnoses.