Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning
This addresses the challenge of transferring segmentation models across medical imaging domains with insufficient annotated data, which is incremental as it builds on existing domain adaptation methods by incorporating human-verified priors.
The paper tackled the problem of domain adaptation for echocardiography segmentation by introducing RL4Seg, a reinforcement learning framework that reduces the need for large annotated datasets and manual review, achieving 99% anatomical validity on expert-validated data and outperforming state-of-the-art methods in accuracy.
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.