Cardiac MRI Left Ventricle Detection by Combining Distance Metric Learning for Proposal Regions and CNN Classification and Regression

Automatic detection of left ventricle(LV)in cardiac MRI is of great value for computer-aided diagnosis of heart disease.To solve the low discrimination problem between left ventricular candidate regions and surrounding tissue,a left ventricular detection method is proposed by combining candidate region two-level distance metric learning and CNN classification and regression joint learning.In the candidate region generation stage,the super-pixel method is employed to generate the initial region and further merged into the intermediate region.The supervised two-level distance metric learning algorithm is designed to fuse the intermediate regions to construct the target candidate regions.In the detection stage,the approach of joint learning with CNN classification and regression is employed to locate candidate regions,and a hard negative mining strategy is added for tuning the model adaptive to the sample imbalance problem.The proposed method and the extended four variant methods(changing or discarding some modules)are performed on the Cardiac Atlas Project(CAP)data set and the results validate the reasonability of module settings in this method.Further experiments show that the proposed method achieves higher detection accuracy in comparison with the Fast R-CNN and SSAE-based methods.