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Segmentation of echocardiograms plays an essential role in the quantitative analysis of the heart and helps diagnose cardiac diseases. In the recent decade, deep learning-based approaches have significantly improved the performance of echocardiogram segmentation. Most deep learning-based methods assume that the image to be processed is rectangular in shape. However, typically echocardiogram images are formed within a sector of a circle, with a significant region in the overall rectangular image where there is no data, a result of the ultrasound imaging methodology. This large non-imaging region can influence the training of deep neural networks. In this paper, we propose to use polar transformation to help train deep learning algorithms. Using the r-θ transformation, a significant portion of the non-imaging background is removed, allowing the neural network to focus on the heart image. The segmentation model is trained on both x-y and r-θ images. During inference, the predictions from the x-y and r-θ images are combined using max-voting. We verify the efficacy of our method on the CAMUS dataset with a variety of segmentation networks, encoder networks, and loss functions. The experimental results demonstrate the effectiveness and versatility of our proposed method for improving the segmentation results. 

There is considerable interest in AI systems that can assist a cardiologist to diagnose echocardiograms, and can also be used to train residents in classifying echocardiograms. Prior work has focused on the analysis of a single frame. Classifying echocardiograms at the video-level is challenging due to intra-frame and inter-frame noise. We propose a two-stream deep network which learns from the spatial context and optical flow for the classification of echocardiography videos. Each stream contains two parts: a Convolutional Neural Network (CNN) for spatial features and a bi-directional Long Short-Term Memory (LSTM) network with Attention for temporal. The features from these two streams are fused for classification. We verify our experimental results on a dataset of 170 (80 normal and 90 abnormal) videos that have been manually labeled by trained cardiologists. Our method provides an overall accuracy of 91:18%, with a sensitivity of 94:11% and a specificity of 88:24%.