Deep Learning Methods for Thyroid Imaging Segmentation: a Systematic Review

Thyroid nodules are highly prevalent, and reliable imaging is essential for diagnosis and treatment. Deep learning (DL) has become the leading approach for automating thyroid ultrasound analysis. Objective: The purpose of this study is to present a systematic review of the state-of-the-art progress in applying deep learning methods to thyroid image segmentation and evaluate their clinical readiness. Method: We conducted a systematic review of 48 primary studies published between 2020 and 2025. workflow steps, DL families, datasets, and deployment contexts are analyzed. Result: Six DL families were identified: U-Net variants, Transformers, Boundary-aware, Weakly/Semi-supervised, Diffusion, and Multimodal fusion. These methods map to a four-step clinical workflow. While many achieved strong performance (Dice $\approx 0.85-0.95$; AUC $>0.90$), most remain research-level ($\sim 60 \%$), with fewer validated in hospitals ($\sim 30 \%$) or pilots ($\sim 10 \%$). Conclusion: DL has advanced thyroid segmentation considerably but faces challenges of limited datasets, reproducibility, and lack of prospective validation. Standardized benchmarks and multi-center studies are needed to move toward clinical adoption.