Automated segmentation and quantitative measurement of cervical nerves in ultrasound images using an SZJ-SEG-based deep learning framework
Year: 2026
Type: article
Abstract: Background: Accurate recognition and quantitative assessment of cervical nerves are essential for ultrasound-guided nerve block, preoperative evaluation, and diagnosis of peripheral neuropathies. However, manual interpretation and measurement of nerve structures remain time-consuming and operator dependent. This study aimed to develop an intelligent, fully automated system for precise segmentation and quantitative measurement of cervical nerves in ultrasound images using a deep learning-based approach. Methods: Ultrasound images from 200 healthy volunteers were collected and meticulously annotated to construct a large-scale standardized dataset comprising 117,729 images. A fully automated analysis framework was designed, incorporating a YOLOv11 network for precise localization of the effective imaging region [mean intersection over union (mIoU) =0.99] and an optical character recognition (OCR) module for automatic extraction of depth scale information. The proposed segmentation network, termed SZJ-SEG, combines a Deconv Block and an efficient upsampling convolution block (EUCB) based on a ResNet50 backbone to achieve high segmentation accuracy with low computational cost. Quantitative analysis of the C5-C7 nerve roots was performed through pixel-to-physical scale calibration for cross-sectional area (CSA) and perimeter calculation. Results: , with mean absolute percentage error (MAPE) between 2.43% and 6.16%, and Pearson correlation coefficients exceeding 0.96. For perimeter measurements, MAE ranged from 0.374 to 0.471 mm, MAPE from 3.05% to 4.49%, and Pearson R from 0.84 to 0.91, indicating excellent consistency with manual annotations. Conclusions: 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.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128715704