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Updated: Nov 4, 2025
Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
Published on: October 28, 2020
Automatic Segmentation of Left Ventricle in Echocardiography Based on YOLOv3 Model to Achieve Constraint and
Zhemin Zhuang1,2, Pengcheng Jin1, Alex Noel Joseph Raj2
1Department of Electronic Engineering, Shantou University, Shantou 515063, China.
Insights
Accurate segmentation of the left ventricle (LV) endocardium is crucial for diagnosing cardiovascular disease (CVD). This study introduces a novel YOLOv3 and MRF model-based method for precise LV endocardium segmentation, improving diagnostic accuracy.
Area of Science:
- Medical Imaging
- Cardiology
- Computer Vision
Background:
- Cardiovascular disease (CVD) poses a significant global health threat with high mortality rates.
- Accurate assessment of cardiac function relies on precise segmentation of cardiac structures, particularly the left ventricle (LV).
- Existing methods for LV endocardium segmentation often face challenges in accuracy and efficiency.
Purpose of the Study:
- To develop and validate an automated method for accurate segmentation of the left ventricle (LV) endocardium.
- To enhance the reliability of cardiac function analysis using myocardial strain and strain rate indicators.
- To improve early diagnosis and prognosis of cardiovascular diseases.
Main Methods:
- Utilized the YOLOv3 model for extracting LV region features and locating key anatomical points (apex, bottom).
- Employed a Markov Random Field (MRF) model for preliminary identification and binarization of the LV myocardium in subimages.
- Applied nonlinear least-squares curve fitting and edge approximation, constrained by identified LV positions, for precise endocardial segmentation.
Main Results:
- Achieved computation speeds of 2.1-2.25 frames per second (fps).
- Demonstrated high segmentation accuracy with Dice coefficient of 93.57 ± 1.97%.
- Reported mean absolute distance (MAD) of 2.57 ± 0.89 mm and Hausdorff distance (HD) of 6.68 ± 1.78 mm.
Conclusions:
- The proposed method offers robust and accurate left ventricle endocardium segmentation.
- This technique shows superior performance compared to existing methods in terms of accuracy and speed.
- Facilitates more reliable cardiac function evaluation for improved cardiovascular disease diagnosis.
Abstract:
Cardiovascular disease (CVD) is the most common type of disease and has a high fatality rate in humans. Early diagnosis is critical for the prognosis of CVD. Before using myocardial tissue strain, strain rate, and other indicators to evaluate and analyze cardiac function, accurate segmentation of the left ventricle (LV) endocardium is vital for ensuring the accuracy of subsequent diagnosis. For accurate segmentation of the LV endocardium, this paper proposes the extraction of the LV region features based on the YOLOv3 model to locate the positions of the apex and bottom of the LV, as well as that of the LV region; thereafter, the subimages of the LV can be obtained, and based on the Markov random field (MRF) model, preliminary identification and binarization of the myocardium of the LV subimages can be realized. Finally, under the constraints of the three aforementioned positions of the LV, precise segmentation and extraction of the LV endocardium can be achieved using nonlinear least-squares curve fitting and edge approximation. The experiments show that the proposed segmentation evaluation indices of the method, including computation speed (fps), Dice, mean absolute distance (MAD), and Hausdorff distance (HD), can reach 2.1-2.25 fps, 93.57 ± 1.97%, 2.57 ± 0.89 mm, and 6.68 ± 1.78 mm, respectively. This indicates that the suggested method has better segmentation accuracy and robustness than existing techniques.
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