RL4Seg3D

Reinforcement-learning-based unsupervised domain adaptation framework for spatio-temporal (2D+time) echocardiography segmentation, extending the authors' earlier RL4Seg work to full-length video sequences. RL4Seg3D uses a sliding-window approach supporting high-resolution, full-sized inputs, and fuses multiple reward mechanisms to improve segmentation reliability without requiring additional expert annotations in the target domain. Trained and evaluated on a large dataset of over 30,000 echocardiography videos, it outperforms baselines and foundation models on overall segmentation accuracy as well as echocardiography-specific metrics including anatomical/temporal validity and mitral-valve-commissure landmark precision, and supports test-time optimization via calibrated uncertainty estimates.