Automated segmentation and quantitative measurement of cervical nerves in ultrasound images using an SZJ-SEG-based deep learning framework

The proposed SZJ-SEG-based system enables accurate, efficient, and reproducible segmentation and quantitative analysis of cervical ultrasound images. With strong performance and high clinical relevance, it provides a reliable tool for ultrasound-guided nerve block localization and preoperative assessment. Its modular architecture offers scalability for extension to other anatomical regions, highlighting broad potential in intelligent ultrasound diagnosis.