A New Semi-Automated Algorithm for Volumetric Segmentation of the Left Ventricle in Temporal 3D Echocardiography Sequences

TL;DR

This paper introduces a semi-automated, minimally interactive algorithm for segmenting the left ventricle in 3D echocardiography sequences, addressing noise challenges without relying on prior models or training data.

Contribution

It presents a novel diffeomorphic registration-based segmentation method that requires minimal user input and outperforms several existing algorithms.

Findings

Achieved a mean absolute difference of 1.01 mm in segmentation.

Obtained a Dice score of 0.93 indicating high overlap accuracy.

Performed well compared to four other registration algorithms.

Abstract

Purpose: Echocardiography is commonly used as a non-invasive imaging tool in clinical practice for the assessment of cardiac function. However, delineation of the left ventricle is challenging due to the inherent properties of ultrasound imaging, such as the presence of speckle noise and the low signal-to-noise ratio. Methods: We propose a semi-automated segmentation algorithm for the delineation of the left ventricle in temporal 3D echocardiography sequences. The method requires minimal user interaction and relies on a diffeomorphic registration approach. Advantages of the method include no dependence on prior geometrical information, training data, or registration from an atlas. Results: The method was evaluated using three-dimensional ultrasound scan sequences from 18 patients from the Mazankowski Alberta Heart Institute, Edmonton, Canada, and compared to manual delineations provided by an expert cardiologist and four other registration algorithms. The segmentation approach yielded the following results over the cardiac cycle: a mean absolute difference of 1.01 (0.21) mm, a Hausdorff distance of 4.41 (1.43) mm, and a Dice overlap score of 0.93 (0.02). Conclusions: The method performed well compared to the four other registration algorithms.