Autoregressive-Conditioned Diffusion for Semi-Supervised Thyroid Ultrasound Segmentation with Optical Flow-Based Pseudo Labels

Ultrasound video segmentation plays a vital role in clinical diagnosis and treatment planning, yet the process is severely hindered by the high cost of manual annotation. Semi-supervised learning leverages limited labels effectively, but suffers from noisy pseudo-labels in lowcontrast frames and lacks global modeling, leading to blurred boundaries and temporal inconsistencies in video segmentation. To this end, we propose a novel semisupervised framework. We begin with a bidirectional optical flow-based pseudo-labeling strategy that fuses forward and backward warped masks using task-aware confidence weighting. To enhance spatial coherence, a diffusion model is employed to denoise the pseudo-labels in the latent space. Most importantly, we introduce a Visual Autoregressive (VAR) Encoder, a Transformer-based temporal modeling module that extracts cross-frame contextual priors from image latents. These priors are injected into every block of a DiT-based diffusion backbone via cross-attention, leveraging DiT’s strong global modeling capacity to refine spatial details while enforcing temporal consistency. Additionally, a YOLO-based post-processing step removes background artifacts by isolating the region of interest (ROI). Experimental results on a newly collected dataset of 80 patients and public datasets demonstrate that our framework achieves superior segmentation accuracy and temporal stability compared to existing semi-supervised methods.