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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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Real-Time Automated Segmentation of Median Nerve in Dynamic Ultrasonography Using Deep Learning.

Cheng-Liang Yeh¹, Chueh-Hung Wu², Ming-Yen Hsiao²

¹Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan.

Ultrasound in Medicine & Biology

|February 5, 2023

Summary

This study introduces an automated deep learning model for real-time median nerve segmentation in dynamic ultrasound, aiding carpal tunnel syndrome (CTS) diagnosis. The model offers efficient and accurate nerve analysis for clinical use.

Keywords:

Automated nerve segmentation Carpal tunnel syndrome Deep learning Dynamic ultrasonography

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Area of Science:

Medical Imaging
Artificial Intelligence
Neurology

Background:

Dynamic ultrasonography is crucial for diagnosing carpal tunnel syndrome (CTS).
Manual segmentation of the median nerve in ultrasound sequences is labor-intensive.
Automated methods are needed for efficient and accurate nerve analysis.

Purpose of the Study:

To develop a real-time, automated deep learning model for median nerve segmentation in dynamic ultrasound.
To enable rapid data extraction for evaluating median nerve morphological dynamics.
To improve the diagnostic workflow for carpal tunnel syndrome.

Main Methods:

A deep learning model, modified from SOLOv2, was developed for median nerve segmentation.
Ensemble strategies were explored to enhance segmentation accuracy.
The model was trained and evaluated on ultrasound data from healthy participants and CTS patients.

Main Results:

The proposed model achieved high segmentation accuracy (IoU 0.855, Dice 0.922) at a fast inference speed (28.9 fps).
It outperformed existing state-of-the-art models in terms of speed while maintaining comparable accuracy.
Ensemble models showed marginal improvements in segmentation accuracy.

Conclusions:

The developed model offers real-time, automated median nerve segmentation for dynamic ultrasound.
This technology has significant potential for clinical application in CTS diagnosis and treatment.
Automated analysis facilitates immediate assessment of median nerve dynamics during image acquisition.