Fully automatic segmentation of heart chambers in cardiac MRI using deep learning

Background Cardiac magnetic resonance imaging (MRI) is now routinely being used for the evaluation of the function and structure of the cardiovascular system. Chamber segmentation from cardiac MRI datasets is an essential step for the computation of clinical indices such as ventricular volume, ejection fraction, mass and wall thickness as well as analysis of wall motion abnormality. Manual delineation by experts is currently the standard clinical practice for performing chamber segmentation. However, manual segmentation is tedious, time consuming and prone to intraand inter-observer variability. Therefore, it is necessary to reproducibly automate this task to accelerate and facilitate the process of diagnosis and follow-up. Due to the several technical difficulties, automatic chamber segmentation from cardiac MRI dataset is still a challenging problem. Current automated techniques suffer from poor robustness and accuracy and require large training datasets and user interaction.