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Published on: February 6, 2020
A Myocardial Segmentation Method Based on Adversarial Learning
Tao Wang1, Juanli Wang1, Jia Zhao2
1Department of Pediatric Cardiovascular Medicine, Xi'an Children's Hospital, Xi'an 710003, China.
Insights
This study introduces an adversarial learning approach to improve automatic myocardial segmentation for 3D cardiovascular magnetic resonance imaging in congenital heart defect patients. The new method enhances segmentation accuracy, aiding surgical planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Congenital heart defects (CHD) require patient-specific 3D heart models for surgical planning, typically generated using cardiovascular magnetic resonance imaging (CMRI).
- Manual segmentation of 3D CMRI data is time-consuming and laborious, limiting clinical application.
- Existing automatic segmentation methods, primarily deep learning-based, face challenges due to image quality issues (inconsistent signals, low contrast) and data scarcity.
Purpose of the Study:
- To develop an improved automatic myocardial segmentation algorithm for 3D CMRI in pediatric CHD patients.
- To address limitations of current segmentation techniques, including image quality challenges and the need for extensive labeled data.
- To enhance the accuracy and efficiency of creating patient-specific 3D heart models for clinical use.
Main Methods:
- Proposed an adversarial learning framework incorporating a discriminant model to provide additional supervision for myocardial segmentation.
- Applied the adversarial learning approach to automatic segmentation of 3D CMRI datasets.
- Utilized real-world datasets for experimental evaluation and comparison against baseline segmentation models.
Main Results:
- The proposed adversarial learning-based method demonstrated improved performance compared to the baseline segmentation model.
- Achieved better results in automatic myocardium segmentation, indicating enhanced accuracy and robustness.
- The integration of adversarial learning effectively addressed some of the inherent challenges in segmenting 3D CMRI data.
Conclusions:
- Adversarial learning offers a promising approach to overcome limitations in automatic myocardial segmentation of 3D CMRI for CHD.
- The developed method shows potential for improving the accuracy and efficiency of 3D heart model generation for surgical planning.
- Further development of such AI-driven tools can significantly benefit the clinical management of pediatric congenital heart defects.
Abstract:
Congenital heart defects (CHD) are structural imperfections of the heart or large blood vessels that are detected around birth and their symptoms vary wildly, with mild case patients having no obvious symptoms and serious cases being potentially life-threatening. Using cardiovascular magnetic resonance imaging (CMRI) technology to create a patient-specific 3D heart model is an important prerequisite for surgical planning in children with CHD. Manually segmenting 3D images using existing tools is time-consuming and laborious, which greatly hinders the routine clinical application of 3D heart models. Therefore, automatic myocardial segmentation algorithms and related computer-aided diagnosis systems have emerged. Currently, the conventional methods for automatic myocardium segmentation are based on deep learning, rather than on the traditional machine learning method. Better results have been achieved, however, difficulties still exist such as CMRI often has, inconsistent signal strength, low contrast, and indistinguishable thin-walled structures near the atrium, valves, and large blood vessels, leading to challenges in automatic myocardium segmentation. Additionally, the labeling of 3D CMR images is time-consuming and laborious, causing problems in obtaining enough accurately labeled data. To solve the above problems, we proposed to apply the idea of adversarial learning to the problem of myocardial segmentation. Through a discriminant model, some additional supervision information is provided as a guide to further improve the performance of the segmentation model. Experiment results on real-world datasets show that our proposed adversarial learning-based method had improved performance compared with the baseline segmentation model and achieved better results on the automatic myocardium segmentation problem.
