3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new intermodality statistical shape modelling method Abstract   Objective  Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new intermodality SSMbased approach to detect the RV endocardium in CMR data.    Methods  Realtime transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semiautomatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with goldstandard (GS) corresponding parameters.    Results  CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2xe2x80xafmin (including manual initialization) per patient. High correlations (r2xe2x80xafu003exe2x80xaf0.76) and not significant bias (BlandAltman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87xe2x80xafxc2xb1xe2x80xaf0.03), acceptable Hausdorff distance (9.35xe2x80xafxc2xb1xe2x80xaf1.51xe2x80xafmm) and small pointtosurface distance (1.91xe2x80xafxc2xb1xe2x80xaf0.26xe2x80xafmm).    Conclusion  A novel SSMbased approach to segment the RV endocardium in CMR scans by using a model trained on 3DEderived RV endocardial surfaces, was proposed. This intermodality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions.

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.