A Semi-Automated Method for Measurement of Left Ventricular Volumes in 3D Echocardiography

Segmentation of the left ventricle in echocardiography data currently poses a challenge, where delineation of the endocardial borders is a time consuming and difficult task. Though semi-automated and fully automated methods have been developed for left ventricular segmentation, they suffer from a number of drawbacks. These drawbacks include the dependence on large sets of training data and assumptions about the distribution of the intensities of the image. This paper proposes a novel volumetric segmentation algorithm based on an angular slicing approach for 3-D echocardiography scans and a diffeomorphic nonrigid registration method. The proposed method is fast, reproducible, and yields a volumetric segmentation with minimal user interaction. The algorithm was evaluated on 30 participants from the challenge on endocardial 3-D ultrasound segmentation dataset from the medical image computing and computer assisted interventions Challenge 2014. The proposed method yielded the following average distance metrics for the end diastolic volumes: 1) mean absolute distance of 2.36 mm, 2) Hausdorff distance of 8.25 mm, and 3) Dice score of 0.887. For the end systolic volumes, the following average distance metrics were obtained: 1) mean absolute distance of 2.33 mm, 2) Hausdorff distance of 8.95 mm, and 3) Dice score of 0.857. The following clinical metrics for the ejection fraction are reported: 1) modified correlation coefficient of 0.169, 2) bias in mL of −3.96 mL, and 3) standard deviation of 6.85 mL. The results demonstrate the robustness of the proposed volumetric segmentation approach.