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Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

Arnaud Judge, Thierry Judge, Nicolas Duchateau, Roman A. Sandler, Joseph Z. Sokol, Olivier Bernard, Pierre-Marc Jodoin

TL;DR

This work tackles domain adaptation for 2D echocardiography segmentation in the presence of scarce target-domain annotations. It introduces RL4Seg, a reinforcement-learning–based framework that leverages a reward network to predict segmentation error and an actor-critic PPO loop to refine segmentations on a large unlabeled target dataset, prompting anatomically valid outputs without expert annotation. The approach yields state-of-the-art accuracy and substantially higher anatomical validity on a targeted test set, while also providing calibrated per-pixel uncertainty estimates via the reward model. RL4Seg thus offers a scalable, annotation-free pathway to anatomically consistent segmentation across domains with practical uncertainty quantification for clinical deployment.

Abstract

Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation.

Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

TL;DR

This work tackles domain adaptation for 2D echocardiography segmentation in the presence of scarce target-domain annotations. It introduces RL4Seg, a reinforcement-learning–based framework that leverages a reward network to predict segmentation error and an actor-critic PPO loop to refine segmentations on a large unlabeled target dataset, prompting anatomically valid outputs without expert annotation. The approach yields state-of-the-art accuracy and substantially higher anatomical validity on a targeted test set, while also providing calibrated per-pixel uncertainty estimates via the reward model. RL4Seg thus offers a scalable, annotation-free pathway to anatomically consistent segmentation across domains with practical uncertainty quantification for clinical deployment.

Abstract

Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation.

Paper Structure

This paper contains 17 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Reinforcement Learning
  4. Segmentation RL
  5. The Policy
  6. The Reward
  7. The Q, Value and Advantage Functions:
  8. The Value operator
  9. RL4Seg
  10. Reward Dataset $\mathcal{D}_r$
  11. Proximal Policy Optimization (PPO)
  12. Uncertainty Estimation
  13. Experiments
  14. Segmentation performance
  15. Uncertainty
  16. ...and 2 more sections

Figures (3)

  • Figure 1: RL4Seg, divided in three sections: [left] the reward dataset creation, [mid] the reward network training, and [right] the fine-tuning of the policy.
  • Figure 2: Results for input images that the baseline was unable to segment correctly.
  • Figure 3: Top: Reliability diagrams and ECE for five uncertainty methods, evaluated on the entire target test set. The dashed line represents perfect calibration. Middle and bottom: Examples of error map and corresponding uncertainty map for each method for segmentations from SOTA models on the target test set.