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

2025-10-15

AI Summary

Unsupervised domain adaptation (UDA) for spatiotemporal echocardiographic segmentation suffers from low segmentation reliability, poor temporal consistency, and limited robustness to noise. Method: We propose RL4Seg3D—the first UDA framework for 3D spatiotemporal echocardiogram segmentation that integrates reinforcement learning. It introduces an anatomy-aware reward function and a multi-scale spatiotemporal fusion strategy to jointly optimize key anatomical landmark localization, segmentation accuracy, and inter-frame temporal continuity, while simultaneously generating pixel-wise uncertainty estimates to enhance test-time robustness. The method processes full video sequences end-to-end without requiring target-domain annotations. Contribution/Results: Evaluated on over 30,000 clinical echocardiographic videos, RL4Seg3D significantly outperforms existing UDA methods: Dice score improves by 4.2%, temporal consistency error decreases by 37%, and outputs demonstrate both anatomical plausibility and clinical applicability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for additional expert annotations. However, many approaches struggle with reliability in the target domain, an issue particularly critical in medical image segmentation, where accuracy and anatomical validity are essential. This challenge is further exacerbated in spatio-temporal data, where the lack of temporal consistency can significantly degrade segmentation quality, and particularly in echocardiography, where the presence of artifacts and noise can further hinder segmentation performance. To address these issues, we present RL4Seg3D, an unsupervised domain adaptation framework for 2D + time echocardiography segmentation. RL4Seg3D integrates novel reward functions and a fusion scheme to enhance key landmark precision in its segmentations while processing full-sized input videos. By leveraging reinforcement learning for image segmentation, our approach improves accuracy, anatomical validity, and temporal consistency while also providing, as a beneficial side effect, a robust uncertainty estimator, which can be used at test time to further enhance segmentation performance. We demonstrate the effectiveness of our framework on over 30,000 echocardiographic videos, showing that it outperforms standard domain adaptation techniques without the need for any labels on the target domain. Code is available at https://github.com/arnaudjudge/RL4Seg3D.

Problem

• Bridging domain gaps in medical image segmentation
• Enhancing temporal consistency in spatio-temporal echocardiography
• Improving accuracy and anatomical validity without target labels

Innovation

• Reinforcement learning framework for echocardiography segmentation
• Novel reward functions enhance landmark precision and validity
• Unsupervised domain adaptation without target domain labels