A deep learning-based approach for automatic segmentation and quantification of the left ventricle from cardiac cine MR images. (April 2020) - Abdeltawab, Hisham; Khalifa, Fahmi; Taher, Fatma; Alghamdi, Norah Saleh; Ghazal, Mohammed; Beache, Garth; Mohamed, Tamer; Keynton, Robert; El-Baz, Ayman
Highlights: The implementation of a novel fully automated method for the segmentation of LV cavity and myocardium and the estimation of physiological heart parameters from short-axis cine cardiac MR images. A fully convolutional neural networks-based approach that has achieved competitive segmentation and LV quantification results when applied on a publicly available dataset (ACDC-2017). A new fully convolutional neural network that has several bottleneck layers that refer to different representation to the input. During cardiac segmentation, a novel loss function called radial loss that minimizes the difference between the predicted LV contours and the ground truth contours was incorporated with the cross-entropy loss. Abstract: Cardiac MRI has been widely used for noninvasive assessment of cardiac anatomy and function as well as heart diagnosis. The estimation of physiological heart parameters for heart diagnosis essentially require accurate segmentation of the Left ventricle (LV) from cardiac MRI. Therefore, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aim to achieve lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Our framework starts by an accurate localization of the LV blood pool center-point using a fully convolutional neural network (FCN) architecture called FCN1. Then, a region of interest (ROI) that contains the LV is extracted from all heart sections. The extracted ROIs are used for the segmentation of LV cavity and myocardium via a novel FCN architecture called FCN2. The FCN2 network has several bottleneck layers and uses less memory footprint than conventional architectures such as U-net. Furthermore, a new loss function called radial loss that minimizes the distance between the predicted and true contours of the LV is introduced into our model. Following myocardial segmentation, functional and mass parameters of the LV are estimated. Automated Cardiac Diagnosis Challenge (ACDC-2017) dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. To sum up, we propose a deep learning approach that can be translated into a clinical tool for heart diagnosis.