A Periodic Frame Learning Approach for Accurate Landmark Localization in M-Mode Echocardiography

Anatomical landmark localization has been a key challenge for medical image analysis. Existing researches mostly adopt CNN as the main architecture for landmark localization while they are not applicable to process image modalities with periodic structure. In this paper, we propose a novel two-stage frame-level detection and heatmap regression model for accurate landmark localization in m-mode echocardiography, which promotes better integration between global context information and local appearance. Specifically, a periodic frame detection module with LSTM is designed to model periodic context and detect frames of systole and diastole from original echocardiography. Next, a CNN based heatmap regression model is introduced to predict landmark localization in each systolic or diastolic local region. Experiment results show that the proposed model achieves average distance error of 9.31, which is at a reduction by 24% comparing to baseline models.