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Updated: Feb 22, 2026
Transthoracic Speckle Tracking Echocardiography for the Quantitative Assessment of Left Ventricular Myocardial Deformation
Published on: October 20, 2016
Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape
Insights
This study introduces an automated method for segmenting myocardial images from myocardial contrast echocardiography (MCE). The new approach improves accuracy for detecting coronary artery disease by integrating shape models with random forests.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Image Segmentation
Background:
- Myocardial contrast echocardiography (MCE) is crucial for assessing left ventricle function and perfusion in coronary artery disease detection.
- Accurate myocardial segmentation is vital for MCE perfusion quantification but is challenging due to noisy, time-varying images.
- Traditional random forests (RF) for segmentation struggle with contextual information and intensity variations.
Purpose of the Study:
- To develop a fully automatic segmentation pipeline for myocardial segmentation in 2-D MCE data.
- To overcome the limitations of classic RF by incorporating shape prior information.
- To enhance the accuracy and robustness of MCE image analysis.
Main Methods:
- A novel pipeline integrating a statistical shape model (SM) with random forests (RF) for myocardial segmentation.
- Incorporation of a shape model feature to improve RF probability maps.
- Refinement of segmentation using SM fitting to probability maps and a bounding box detection preprocessing step.
- Extension to 2-D+t sequences for temporal consistency.
Main Results:
- The proposed method significantly improves segmentation accuracy on clinical MCE datasets.
- The integration of shape models enhances RF performance by providing contextual and shape prior information.
- The pipeline outperforms existing state-of-the-art methods, including classic RF, active shape models, and image registration.
Conclusions:
- The developed automatic segmentation pipeline effectively addresses limitations of classic RF for MCE analysis.
- The novel approach using shape models enhances myocardial segmentation accuracy and robustness.
- This method offers a significant advancement for automated MCE perfusion quantification and coronary artery disease detection.
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 2-D 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 2-D image is further extended to 2-D+t sequences which ensures temporal consistency in the final sequence segmentations. When evaluated on clinical MCE data sets, 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.
