A motion aware DNN model with edge focus loss and quality control for short-axis left ventricle segmentation of cine MR sequences

Motivation: We propose a motion aware DNN model for cardiac sequence segmentation. Goal(s): We construct an in-house dataset which has three advantages: segmentation annotations covering the cardiac cycle; comprehensive annotations, including the annotations of interventricular groove structure; fine annotations of endocardium. Approach: We propose an edge focus loss to make the segmented boundaries be consistent with the local gradient of the input images and propose a quality control method based on Image Moments to filter abnormal predictions. Results: The experimental results highlight the accuracy of the proposed model, and the fine segmentation results could be used to estimate accurate clinical indicators for clinical diagnosis. Impact: In experiments, we compare the proposed model with 12 state-of-the-art segmentation models, and our model have obtained the highest accuracy for the segmentation and the highest PCC on the 17-segment model.