Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation

Area of Science:

• Medical image analysis
• Machine learning
• Cardiovascular imaging

Background:

• Domain adaptation is crucial for medical image segmentation, reducing annotation needs.
• Existing methods lack reliability in target domains, especially for spatio-temporal data like echocardiography.
• Artifacts and noise in echocardiography further challenge segmentation performance.

Purpose of the Study:

• To develop an unsupervised domain adaptation framework for 2D + time echocardiography segmentation.
• To enhance accuracy, anatomical validity, and temporal consistency in segmentations.
• To provide a robust uncertainty estimation for improved segmentation performance.

Main Methods:

• RL4Seg3D framework utilizes reinforcement learning for unsupervised domain adaptation in echocardiography.
• Novel reward functions and a fusion scheme are integrated to improve key landmark precision.
• The approach processes full-sized input videos, addressing spatio-temporal challenges.

Main Results:

• RL4Seg3D outperforms standard domain adaptation techniques on over 30,000 echocardiographic videos.
• The method achieves improved accuracy, anatomical validity, and temporal consistency.
• A robust uncertainty estimator is provided as a beneficial side effect.

Conclusions:

• RL4Seg3D offers an effective unsupervised domain adaptation solution for echocardiography segmentation.
• The framework enhances segmentation reliability without requiring target domain labels.
• The developed uncertainty estimator can further improve segmentation performance at test time.