A computational model-based approach for atlas construction of aortic Doppler velocity profiles for segmentation purposes

Abstract

Echocardiography is the leading imaging modality for cardiac disorders in clinical practice. During an echocardiographic exam, geometry and blood flow are quantified in order to assess cardiac function. In clinical practice, these imagebased measurements are currently performed manually. An automated approach is needed if more advanced analysis is desired. In this article, we propose a new hybrid framework for the construction of a disease-specific atlas to improve Doppler aortic outflow velocity profile segmentation. The proposed method is based on combining realistic computational simulations of the cardiovascular system for common cardiac conditions (using Cir-cAdapt) with a validated image-based atlas construction method. The coupling is realized via model-based generation of echocardiographic images of virtual populations with a statistically approved parameter variation. We created virtual populations of 100 healthy individuals and 100 patients with aortic stenosis, synthesized their aortic Doppler velocity images and constructed the corresponding atlases. We validated atlases' performances by comparing their segmentation of real clinical images with the manually segmented ground truth. The experimental results show that the segmentation accuracy obtained using the proposed atlases is comparable to the accuracy obtained using classical clinical image-based atlases. Moreover, this framework eliminates the time-consuming acquisition of a sufficient number of representative images in clinical practice, offering a substantial time efficiency and flexibility in creating a disease specific atlas and ensuring an observer-independent automated segmentation. The proposed approach can easily be extended towards the creation of atlases for segmenting any Doppler trace in the cardiovascular circulation in a specific disease.

A computer vision pipeline for fully automated echocardiogram interpretation

“…Several studies have targeted automated methods for spectral envelope segmentation and interpretation of Doppler images using signal processing and machine learning approaches. Such as low-level image-processing based methods [35,42], texture filter analysis [219] and thresholding and edge detection [43][44][45][46][47][48], contour-based and model-based methods for Doppler segmentation [36][37][38][39]49] and traditional machine learning [40,41].…”

“…• measuring peak velocitites from Tissue Doppler Imaging (TDI) images [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49] (Chapter…”