  Localization of anatomical landmarks to perform two-dimensional measurements
in echocardiography is part of routine clinical workflow in cardiac disease
diagnosis. Automatic localization of those landmarks is highly desirable to
improve workflow and reduce interobserver variability. Training a machine
learning framework to perform such localization is hindered given the sparse
nature of gold standard labels; only few percent of cardiac cine series frames
are normally manually labeled for clinical use. In this paper, we propose a new
end-to-end reciprocal detection and tracking model that is specifically
designed to handle the sparse nature of echocardiography labels. The model is
trained using few annotated frames across the entire cardiac cine sequence to
generate consistent detection and tracking of landmarks, and an adversarial
training for the model is proposed to take advantage of these annotated frames.
The superiority of the proposed reciprocal model is demonstrated using a series
of experiments.
