ArticleUltrasonography (Seoul, Korea)2022
Scale-attentional U-Net for the segmentation of the median nerve in ultrasound images.
Article in Ultrasonography (Seoul, Korea), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Masked autoencoder pretraining for peripheral nerve segmentation in ultrasound images.Scientific reports · 2026Article
- Automated Segmentation of Median Nerve Cross-Sectional Area in Healthy Controls and Patients With Carpal Tunnel Syndrome Using a Convolutional Neural Network.Muscle & nerve · 2026Article
- Deep Learning-Based Segmentation of the Ulnar Nerve in Ultrasound Images.Medicina (Kaunas, Lithuania) · 2026Article
- Investigation of Appropriate Scaling of Networks and Images for Convolutional Neural Network-Based Nerve Detection in Ultrasound-Guided Nerve Blocks.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence-Supported Ultrasonography in Anesthesiology: Evaluation of a Patient in the Operating Theatre.Journal of personalized medicine · 2024Review
- Deep-learning segmentation of fascicles from microCT of the human vagus nerve.Frontiers in neuroscience · 2023Article
- From Visualization to Automation: A Narrative Review of Deep Learning's Impact on Ultrasound-based Median Nerve Assessment.Journal of medical ultrasoundReview
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Authors and funding
5 authors.
Funding
Abstract
purposeThe aim of this study was to develop a neural network that accurately and effectively segments the median nerve in ultrasound (US) images.
methodsIn total, 1,305 images of the median nerve of 123 normal subjects were used to train and evaluate the model. Four datasets from two measurement regions (wrist and forearm) of the nerve and two US machines were used. The neural network was designed for high accuracy by combining information at multiple scales, as well as for high efficiency to prevent overfitting. The model was designed in two parts (cascaded and factorized convolutions), followed by selfattention over scale and channel features. The precision, recall, dice similarity coefficient (DSC), and Hausdorff distance (HD) were used as performance metrics. The area under the receiver operating characteristic curve (AUC) was also assessed.
resultsIn the wrist datasets, the proposed network achieved 92.7% and 90.3% precision, 92.4% and 89.8% recall, DSCs of 92.3% and 89.7%, HDs of 5.158 and 4.966, and AUCs of 0.9755 and 0.9399 on two machines. In the forearm datasets, 79.3% and 87.8% precision, 76.0% and 85.0% recall, DSCs of 76.1% and 85.8%, HDs of 5.206 and 4.527, and AUCs of 0.8846 and 0.9150 were achieved. In all datasets, the model developed herein achieved better performance in terms of DSC than previous U-Net-based systems.
conclusionThe proposed neural network yields accurate segmentation results to assist clinicians in identifying the median nerve.
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