Automated Detection of Apical Foreshortening in Echocardiography Using Statistical Shape Modelling

Automated detection of foreshortening, a common challenge in routine 2-D echocardiography, has the potential to improve quality of acquisitions and reduce the variability of left ventricular measurements. Acquiring and labelling the required training data is challenging due to the time-intensive and highly subjective nature of foreshortened apical views. We aimed to develop an automatic pipeline for the detection of foreshortening. To this end, we propose a method to generate synthetic apical-four-chamber (A4C) views with matching ground truth foreshortening labels.

A statistical shape model of the four chambers of the heart was used to synthesise idealised A4C views with varying degrees of foreshortening. Contours of the left ventricular endocardium were segmented in the images, and a partial least squares (PLS) model was trained to learn the morphological traits of foreshortening. The predictive capability of the learned synthetic features was evaluated on an independent set of manually labelled and automatically curated real echocardiographic A4C images.

Acceptable classification accuracy for identification of foreshortened views in the testing set was achieved using logistic regression based on 11 PLS shape modes, with a sensitivity, specificity and area under the receiver operating characteristic curve of 0.84, 0.82 and 0.84, respectively. Both synthetic and real cohorts showed interpretable traits of foreshortening within the first two PLS shape modes, reflected as a shortening in the long-axis length and apical rounding.

A contour shape model trained only on synthesized A4C views allowed accurate prediction of foreshortening in real echocardiographic images.