  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 tracking
is still a challenging task. In addition to fuzzy border definition, low
contrast, and abounding artifacts on typical ultrasound images, the shape and
size of the LV change significantly in a cardiac cycle. In this work, we
propose a temporal affine network (TAN) to perform image analysis in a warped
image space, where the shape and size variations due to the cardiac motion as
well as other artifacts are largely compensated. Furthermore, we perform three
frequent echocardiogram interpretation tasks simultaneously: standard cardiac
plane recognition, LV landmark detection, and LV segmentation. Instead of using
three networks with one dedicating to each task, we use a multi-task network to
perform 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 to
enhance motion coherence in the segmentation result. Experiments on 1,714 2D
echocardiographic sequences demonstrate that the proposed method achieves
state-of-the-art segmentation accuracy with real-time efficiency.
