Spatio-Temporal Segmentation in 3D Echocardiographic Sequences using Fractional Brownian Motion

TL;DR

This paper introduces a novel fully-automated 3D echocardiographic segmentation method for the left ventricle using fractional Brownian motion, capturing fractal-like boundary characteristics for improved cardiac analysis.

Contribution

It presents a new fBm-based segmentation approach that leverages the fractal nature of LV boundaries, enhancing accuracy over existing methods.

Findings

Benchmarking shows improved accuracy over state-of-the-art methods.

Validated on synthetic and real canine data with expert annotations.

Potential for clinical application in cardiovascular diagnosis.

Abstract

An important aspect for an improved cardiac functional analysis is the accurate segmentation of the left ventricle (LV). A novel approach for fully-automated segmentation of the LV endocardium and epicardium contours is presented. This is mainly based on the natural physical characteristics of the LV shape structure. Both sides of the LV boundaries exhibit natural elliptical curvatures by having details on various scales, i.e. exhibiting fractal-like characteristics. The fractional Brownian motion (fBm), which is a non-stationary stochastic process, integrates well with the stochastic nature of ultrasound echoes. It has the advantage of representing a wide range of non-stationary signals and can quantify statistical local self-similarity throughout the time-sequence ultrasound images. The locally characterized boundaries of the fBm segmented LV were further iteratively refined using global information by means of second-order moments. The method is benchmarked using synthetic 3D+time echocardiographic sequences for normal and different ischemic cardiomyopathy, and results compared with state-of-the-art LV segmentation. Furthermore, the framework was validated against real data from canine cases with expert-defined segmentations and demonstrated improved accuracy. The fBm-based segmentation algorithm is fully automatic and has the potential to be used clinically together with 3D echocardiography for improved cardiovascular disease diagnosis.