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An efficient deep landmark detection network for PLAX EF estimation using sparse annotations

The parasternal long axis (PLAX) is a routine imaging plane used by clinicians to assess the overall function of the left ventricle during an echocardiogram. Measurements from the PLAX view, in particular left ventricle internal dimension at both end-diastole (LVIDd) and end-systole (LVIDs), are significant markers used to identify cardiovascular disease and can provide an estimation of ejection fraction (EF). However, due to the user-dependent nature of echocardiograms, these measurements suffer from a large amount of inter-observer variability, which greatly affect the sensitive formula used to calculate PLAX EF. While few previous works have attempted to reduce this variability by automating LVID measurements, their models not only lack reliable accuracy and precision, but also generally are not suited to be adapted in point-of-care ultrasound (POCUS) which has limited computing resources. In this paper, we propose a fully automatic, light-weight landmark detection network for detecting LVID and rapidly estimating PLAX EF. Our model is built upon recent advances in deep video landmark tracking with extremely sparse annotations.1 The model is trained on only two frames in the cardiac cine that contain either the LVIDd or LVIDs measurements labeled by clinicians. Using data from 34,305 patients for our experiments, the proposed model accurately tracks the contraction of left ventricular walls. Our model achieves a mean absolute error and standard deviation of 2:65 ± 2:36 mm, 2:77 ± 2:58 mm, and 8:45 ± 7:43% for predicting LVIDd length, LVIDs length, and PLAX EF, respectively. As a light-weight network with less than 125,000 parameters, our model is extremely accessible for POCUS applications.