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

Arnaud Judge, Nicolas Duchateau, Thierry Judge

IEEE Transactions on Medical Imaging

|May 15, 2026

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This study introduces RL4Seg3D, a novel unsupervised domain adaptation method for echocardiography segmentation. It improves accuracy and temporal consistency in medical image segmentation without requiring target domain labels.

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.

Related Experiment Videos

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.