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Comparative studies of deep learning segmentation models for left ventricle segmentation
Muhammad Ali Shoaib1,2, Khin Wee Lai3, Joon Huang Chuah1
1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur, Malaysia.
Insights
This study introduces automated left ventricle (LV) segmentation using deep learning. Mask R-CNN achieved superior performance, demonstrating the potential for improved cardiovascular disease diagnosis.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Cardiovascular disease is a leading cause of death.
- Left ventricle (LV) segmentation from 2D echocardiography is crucial for heart function analysis and disease detection.
- Accurate LV segmentation is vital for understanding cardiac anatomy and identifying abnormalities.
Purpose of the Study:
- To develop and evaluate automated LV segmentation methods using deep learning.
- To compare the performance of SegNet, Fully Convolutional Network, and Mask R-CNN for LV segmentation.
- To assess the impact of training data size on segmentation accuracy.
Main Methods:
- Implementation of three convolutional neural network architectures: SegNet, Fully Convolutional Network, and Mask R-CNN.
- Generation of an echocardiography image dataset for training and evaluation.
- Performance evaluation using metrics: pixel accuracy, precision, recall, specificity, Jaccard index, and Dice Similarity Coefficient (DSC).
Main Results:
- Mask R-CNN demonstrated superior performance compared to SegNet and Fully Convolutional Network across all evaluation metrics.
- With 4,000 training images, Mask R-CNN achieved a DSC of 92.21%, Jaccard index of 85.55%, and high values for accuracy, recall, precision, and specificity.
- Segmentation performance stabilized with over 4,000 training images.
Conclusions:
- Automated LV segmentation using deep learning, particularly Mask R-CNN, offers a promising approach for clinical applications.
- The Mask R-CNN model provides accurate and reliable LV segmentation, aiding in cardiovascular disease assessment.
- Sufficient training data (over 4,000 images) is essential for achieving stable and high-performance automated segmentation.
Abstract:
One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart. Therefore, LV segmentation is an important and critical task in medical practice, and automated LV segmentation is a pressing need. The deep learning models have been utilized in research for automatic LV segmentation. In this work, three cutting-edge convolutional neural network architectures (SegNet, Fully Convolutional Network, and Mask R-CNN) are designed and implemented to segment the LV. In addition, an echocardiography image dataset is generated, and the amount of training data is gradually increased to measure segmentation performance using evaluation metrics. The pixel's accuracy, precision, recall, specificity, Jaccard index, and dice similarity coefficients are applied to evaluate the three models. The Mask R-CNN model outperformed the other two models in these evaluation metrics. As a result, the Mask R-CNN model is used in this study to examine the effect of training data. For 4,000 images, the network achieved 92.21% DSC value, 85.55% Jaccard index, 98.76% mean accuracy, 96.81% recall, 93.15% precision, and 96.58% specificity value. Relatively, the Mask R-CNN outperformed other architectures, and the performance achieves stability when the model is trained using more than 4,000 training images.
