An Automatic Cardiac Segmentation Framework Based on Multisequence MR Image. LGE CMR is an efficient technology for detecting infarcted myocardium. An efficient and objective ventricle segmentation method in LGE can benefit the location of the infarcted myocardium. In this paper, we proposed an automatic framework for LGE image segmentation. There are just 5 labeled LGE volumes with about 15 slices of each volume. We adopted histogram match, an invariant of rotation registration method, on the other labeled modalities to achieve effective augmentation of the training data. A CNN segmentation model was trained based on the augmented training data by leaveoneout strategy. The predicted result of the model followed a connected component analysis for each class to remain the largest connected component as the final segmentation result. Our model was evaluated by the 2019 Multisequence Cardiac MR Segmentation Challenge. The mean testing result of 40 testing volumes on Dice score, Jaccard score, Surface distance, and Hausdorff distance is 0.8087, 0.6976, 2.8727 mm, and 15.6387 mm, respectively. The experiment result shows a satisfying performance of the proposed framework. Code is available at https:github.comSuiiyuMSCMR2019.

KnowledgeBased Multisequence MR Segmentation via Deep Learning with a Hybrid UNet Model The accurate segmentation, analysis and modelling of ventricles and myocardium plays a significant role in the diagnosis and treatment of patients with myocardial infarction (MI). Magnetic resonance imaging (MRI) is specifically employed to collect imaging anatomical and functional information about the cardiac. In this paper, we have proposed a segmentation framework for the MSCMRSeg Multisequence Cardiac MR Segmentation Challenge, which can extract the desired regions and boundaries. In our framework, we have designed a binary classifier to improve the accuracy of the left ventricles (LVs). Extensive experiments on both validation dataset and testing dataset demonstrate the effectiveness of this strategy and give an insight towards the future work.