SegUnXt+: A High-Performance Deep Learning Model for Thyroid Segmentation in Fully Automatic 3D USE Robotic Examination System.

Thyroid cancer, a prevalent malignancy with rising incidence-especially among females-requires early detection to reduce overdiagnosis risks and enable less invasive treatments. Ultrasound (US) imaging is pivotal but limited by observer variability. We propose a 3D imaging system combining a US machine, robotic arm, depth camera, and customized software to address this. The system integrates automated robotic assistance for precision and repeatability, alongside 3D reconstruction of ultrasound elastography (USE) and brightness mode (USB) for flexible multi-view observation and quantitative stiffness analysis. Our system incorporates SegUnXt+, a deep learning model demonstrating competitive thyroid gland segmentation (IoU 82.8%, DC 90.6%) and thyroid nodule segmentation (IoU 71.9%, DC 83.3%), outperforming other models. The system enhances diagnostic accuracy by minimizing observer dependency, enabling precise 3D thyroid visualization, and supporting early detection through automated, quantitative elastographic and morphologic analysis.Clinical Relevance- Mass screening and support diagnosis of thyroid cancer to improve screening accuracy and accessibility.