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2D echocardiography is the most common imaging modality for cardiovasculardiseases. The portability and relatively low-cost nature of Ultrasound (US)enable the US devices needed for performing echocardiography to be made widelyavailable. However, acquiring and interpreting cardiac US images is operatordependent, limiting its use to only places where experts are present. Recently,Deep Learning (DL) has been used in 2D echocardiography for automated viewclassification, and structure and function assessment. Although these recentworks show promise in developing computer-guided acquisition and automatedinterpretation of echocardiograms, most of these methods do not model andestimate uncertainty which can be important when testing on data coming from adistribution further away from that of the training data. Uncertainty estimatescan be beneficial both during the image acquisition phase (by providingreal-time feedback to the operator on acquired image's quality), and duringautomated measurement and interpretation. The performance of uncertainty modelsand quantification metric may depend on the prediction task and the modelsbeing compared. Hence, to gain insight of uncertainty modelling for leftventricular segmentation from US images, we compare three ensembling baseduncertainty models quantified using four different metrics (one newly proposed)on state-of-the-art baseline networks using two publicly availableechocardiogram datasets. We further demonstrate how uncertainty estimation canbe used to automatically reject poor quality images and improvestate-of-the-art segmentation results.

Uncertainty Estimation in Deep 2D Echocardiography Segmentation