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With superiorities on low cost, portability, and free of radiation,echocardiogram is a widely used imaging modality for left ventricle (LV)function quantification. However, automatic LV segmentation and motion trackingis still a challenging task. In addition to fuzzy border definition, lowcontrast, and abounding artifacts on typical ultrasound images, the shape andsize of the LV change significantly in a cardiac cycle. In this work, wepropose a temporal affine network (TAN) to perform image analysis in a warpedimage space, where the shape and size variations due to the cardiac motion aswell as other artifacts are largely compensated. Furthermore, we perform threefrequent echocardiogram interpretation tasks simultaneously: standard cardiacplane recognition, LV landmark detection, and LV segmentation. Instead of usingthree networks with one dedicating to each task, we use a multi-task network toperform three tasks simultaneously. Since three tasks share the same encoder,the compact network improves the segmentation accuracy with more supervision.The network is further finetuned with optical flow adjusted annotations toenhance motion coherence in the segmentation result. Experiments on 1,714 2Dechocardiographic sequences demonstrate that the proposed method achievesstate-of-the-art segmentation accuracy with real-time efficiency.

TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos