Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model

TL;DR

This paper presents a fully automatic myocardial segmentation method for contrast echocardiography sequences that combines random forests with a shape model to improve accuracy and temporal consistency in noisy, variable images.

Contribution

It introduces a novel integration of a statistical shape model with random forests, enhancing segmentation accuracy and robustness in myocardial imaging.

Findings

Outperforms state-of-the-art segmentation methods

Achieves higher accuracy on clinical data

Ensures temporal consistency in sequence segmentation

Abstract

Myocardial contrast echocardiography (MCE) is an imaging technique that assesses left ventricle function and myocardial perfusion for the detection of coronary artery diseases. Automatic MCE perfusion quantification is challenging and requires accurate segmentation of the myocardium from noisy and time-varying images. Random forests (RF) have been successfully applied to many medical image segmentation tasks. However, the pixel-wise RF classifier ignores contextual relationships between label outputs of individual pixels. RF which only utilizes local appearance features is also susceptible to data suffering from large intensity variations. In this paper, we demonstrate how to overcome the above limitations of classic RF by presenting a fully automatic segmentation pipeline for myocardial segmentation in full-cycle 2D MCE data. Specifically, a statistical shape model is used to provide shape prior information that guide the RF segmentation in two ways. First, a novel shape model (SM) feature is incorporated into the RF framework to generate a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to refine and constrain the final segmentation to plausible myocardial shapes. We further improve the performance by introducing a bounding box detection algorithm as a preprocessing step in the segmentation pipeline. Our approach on 2D image is further extended to 2D+t sequence which ensures temporal consistency in the resultant sequence segmentations. When evaluated on clinical MCE data, our proposed method achieves notable improvement in segmentation accuracy and outperforms other state-of-the-art methods including the classic RF and its variants, active shape model and image registration.