Learning Anatomical Segmentation through Reasoning-Augmented Iterative State Transition Networks

Accurate left ventricle (LV) segmentation from cardiac magnetic resonance (CMR) images remains challenging owing to anatomical variability, ambiguous boundaries, and large appearance changes across cardiac phases. Existing segmentation networks typically formulate segmentation as a one-pass prediction problem, limiting their ability to progressively correct intermediate errors. To address this limitation, we propose the Reasoning-Augmented Self-correcting Memory Network (RASM), a novel iterative state-transition learning framework that progressively refines the segmentation through iterative residual state transitions. The proposed framework integrates latent anatomical feature encoding, iterative state-transition learning, adaptive anatomical feature refinement, and multi-stage deep supervision within a unified end-to-end optimization framework. By sharing parameters across refinement stages, RASM progressively improves segmentation quality while maintaining efficient inference. Extensive experiments on the public ACDC benchmark demonstrate that the proposed method consistently outperforms representative CNN-based, Transformer-based, and hybrid segmentation models in both segmentation accuracy and boundary delineation. These results demonstrate the effectiveness of iterative state-transition learning as a general paradigm for robust medical image segmentation.