A TwoStage Fully Automatic Segmentation Scheme Using Both 2D and 3D UNet for Multisequence Cardiac MR. Multisequence cardiac magnetic resonance (MR) segmentation is an important medical imaging technology that facilitates intelligent interpretation of clinical MR images. However, fully automatic segmentation of multisequence cardiac MR is a challenging task due to the complexity and variability of cardiac anatomy. In this study, we propose a twostage deep learning scheme for automatic segmentation of volumetric multisequence MR images by leveraging both 2D and 3D UNet. In the first stage, a 2D UNet model coupled with the iterative randomized Hough transform is employed on the balancedsteady state free precession (bSSFP) MR sequences, so as to find the center coordinates of the left ventricles (LVs). The regions of interest (ROIs) are then localized around the center coordinates on the corresponding late gadolinium enhanced (LGE) MR sequences. In the second stage, a 3D probabilistic UNet model is performed on the ROIs in the LGE data to segment the LV, right ventricle (RV) and left ventricular myocardium (MYO). Experimental results on the MICCAI 2019 MultiSequence Cardiac MR Segmentation (MSCMRSeg) Challenge show that the proposed scheme performs well with average Dice similarity coefficients of LV, RV and MYO as 0.792, 0.697 and 0.611, respectively.

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.