CCE Faculty Articles

AI for Automated Segmentation and Characterization of Median Nerve Volume

Document Type

Article

Publication Title

Journal of Medical and Biological Engineering

ISSN

2199-4757

Publication Date

8-4-2013

Abstract

Purpose

Carpal tunnel syndrome (CTS) is characterized anatomically by enlargement of the median nerve (MN) at the wrist. To better understand the 3D morphology and volume of the enlargement, we studied its volume using automated segmentation of ultrasound (US) images in 10 volunteers and 4 patients diagnosed with CTS.

Method

US images were acquired axially for a 4 cm MN segment from the proximal carpal tunnel region to mid-forearm in 10 volunteers and 4 patients with CTS, yielding over 18,000 images. We used U-Net with ConvNet blocks to create a model of MN segmentation for CTS study, compared to manual measurements by two readers.

Results

The average Dice Similarity Coefficient (DSC) on the internal and external validation datasets was 0.82 and 0.81, respectively, and the area under the curve (AUC) was 0.92 and 0.88, respectively. The inter-reader correlation DSC was 0.83, and the AUC was 0.98. The correlation between U-Net and manual tracing was best when the MN was near the surface. A US phantom mimicking the MN, imaged at varied scanning speeds from 7 to 45 mm/s, showed the volume measurements were consistent.

Conclusion

Our AI model effectively segmented the MN to calculate MN volume, which can now be studied as a potential biomarker for CTS, along with the already established biomarker, cross-sectional area.

DOI

10.1007/s40846-023-00805-z

Volume

43

First Page

405

Last Page

416

Comments

We would like to thank the Mayo Clinic Department of Radiology for computational resources and the Department of Orthopedic for data collection.

Funding for this work was provided by Mayo Clinic and a grant from NIH/NIAMS (AR62613). NIH/NIAMS had no involvement in study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article.

© Taiwanese Society of Biomedical Engineering 2023

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