Left ventricle automatic segmentation in cardiac MRI using a combined CNN and U-net approach

Cardiovascular diseases can be effectively prevented from worsening through early diagnosis. To this end, various methods have been proposed to detect the disease source by analyzing cardiac magnetic resonance images (MRI), wherein left ventricular segmentation plays an indispensable role. However, since the left ventricle (LV) is easily confused with other regions in cardiac MRI, segmentation of the LV is a challenging problem. To address this issue, we propose a composite model combining CNN and U-net to accurately segment the LV. In our model, CNN is used to locate the region of interest (ROI) and the U-net network achieve segmentation of LV. We used the cardiac MRI datasets of the MICCAI 2009 left ventricular segmentation challenge to train and test our model and demonstrated the accuracy and robustness of the proposed model. The proposed model achieved state-of-the-art results. The metrics are Dice metric (DM), volumetric overlap error (VOE) and Hausdorff distance (HD), in which DM reaches 0.951, VOE reaches 0.053 and HD reaches 3.641.