Cross-Domain Echocardiography Segmentation with Multi-Space Joint Adaptation.

Area of Science:

• Medical imaging analysis
• Artificial intelligence in healthcare
• Cardiovascular diagnostics

Background:

• Accurate segmentation of left ventricle endocardium (LV_endo) and epicardium (LV_epi) in echocardiography is crucial for clinical diagnosis.
• Deep neural networks are prevalent for echocardiography segmentation but struggle with domain shift in unseen datasets.
• Domain adaptation algorithms are necessary to improve the generalization of deep learning models across different data distributions.

Purpose of the Study:

• To present a novel multi-space adaptation-segmentation-joint framework (MACS) for robust cross-domain echocardiography segmentation.
• To address the performance degradation of segmentation networks on unseen datasets due to data distribution shifts.
• To enhance the generalization capability of deep learning models in echocardiography analysis.

Main Methods:

• Developed a generative adversarial architecture for joint adaptation and segmentation.
• Employed a generator for the segmentation task and multi-space discriminators for domain alignment in feature and output spaces.
• Evaluated the MACS method on two distinct echocardiography datasets from different medical centers and vendors.

Main Results:

• The MACS method demonstrated effective handling of unseen domain datasets.
• The framework improved generalization performance by 2.2% in the Dice metric.
• MACS achieved these improvements without requiring manual annotations on target datasets.

Conclusions:

• The proposed MACS framework significantly enhances the generalization of deep learning-based echocardiography segmentation across different domains.
• MACS offers a practical solution for improving diagnostic accuracy by overcoming data variability challenges in clinical settings.
• The method's ability to adapt without manual annotation makes it a valuable tool for real-world echocardiography analysis.