Automated segmentation of thyroid tissue and carotid artery in ultrasound videos using expert-in-the-loop deep learning: a foundational step toward AI-assisted thyroid diagnostics.
TL;DR
This multi-structure, video-based segmentation approach provides a clinically relevant foundation for artificial intelligence-assisted thyroid imaging, with potential applications in anatomical localisation, thyroid volume estimation, procedural guidance, three-dimensional reconstruction, and image-guided or robotic ultrasound systems.
Abstract
Ultrasound (US) is the primary imaging modality for thyroid evaluation, yet its diagnostic accuracy remains highly operator dependent. Automated identification of thyroid anatomy and adjacent vascular structures may improve standardisation, support image-guided procedures, and enhance preoperative decision-making in endocrine surgery. In this study, 300 B-mode ultrasound videos from 280 patients (111,679 frames) were used to develop a deep learning-based segmentation model for simultaneous delineation of the thyroid gland and carotid artery. Data were split at the patient level into training/validation (n = 240) and an independent test set (n = 60). An expert-in-the-loop annotation workflow combining manual annotation, model-assisted pseudo-labelling, and expert refinement was implemented to enable efficient dataset construction. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and intersection over union (IoU). On the independent test set, the model achieved a DSC of 0.96 ± 0.02 and 0.96 ± 0.03 for the two segmented structures, with corresponding IoU values of 0.93 ± 0.03 and 0.93 ± 0.05. The model maintained consistent performance across varying anatomical presentations, including pathological nodules. Separately, evaluation of the expert-in-the-loop annotation pipeline showed high agreement between model-generated pseudo-labels and final expert-corrected annotations, indicating that the semi-automatic approach provided a strong initial approximation requiring only limited refinement. This multi-structure, video-based segmentation approach provides a clinically relevant foundation for artificial intelligence-assisted thyroid imaging, with potential applications in anatomical localisation, thyroid volume estimation, procedural guidance, three-dimensional reconstruction, and image-guided or robotic ultrasound systems.