ArticleFrontiers in neurology2018
Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach.
Article in Frontiers in neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 42 citations in OpenAlex.
- A feasibility study of deep learning-based segmentation of the inferior alveolar nerve on magnetic resonance neurography.Scientific reports · 2026Article
- A novel workflow for 3D imaging and spatial analysis of nerves in bone.Bone reports · 2025Article
- Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Automated peripheral nerve segmentation for MR-neurography.European radiology experimental · 2024Article
- Quantitative double echo steady state T2 mapping of upper extremity peripheral nerves and muscles.Frontiers in neurology · 2024Article
- Deep-learning segmentation of fascicles from microCT of the human vagus nerve.Frontiers in neuroscience · 2023Article
- The promise and limitations of artificial intelligence in musculoskeletal imaging.Frontiers in radiology · 2023Review
- New insights into the evaluation of peripheral nerves lesions: a survival guide for beginners.Neuroradiology · 2022Review
- Medical-Blocks-A Platform for Exploration, Management, Analysis, and Sharing of Data in Biomedical Research: System Development and Integration Results.JMIR formative research · 2022Article
- Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches.Skeletal radiology · 2022Article
- AI MSK clinical applications: spine imaging.Skeletal radiology · 2022Review
- Quantitative MR-Neurography at 3.0T: Inter-Scanner Reproducibility.Frontiers in neuroscience · 2022Article
- Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.Frontiers in oncology · 2022Review
- Artificial intelligence in brachytherapy: a summary of recent developments.The British journal of radiology · 2021Review
- Recent Advances of Magnetic Resonance Neuroimaging in Trigeminal Neuralgia.Current pain and headache reports · 2021Review
- Peripheral Nerve Diffusion Tensor Imaging : Interreader and Test-retest Reliability as Quantified by the Standard Error of Measurement.Clinical neuroradiology · 2020Article
- Radiomics of peripheral nerves MRI in mild carpal and cubital tunnel syndrome.La Radiologia medica · 2020Article
- Technological Advancements in Magnetic Resonance Neurography.Current neurology and neuroscience reports · 2019Review
- Peripheral nerve magnetic resonance imaging.F1000Research · 2019Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic studies, and since recently magnetic resonance neurography (MRN). The aim of this study was to develop and evaluate a fully-automatic segmentation method of peripheral nerves of the thigh. T2-weighted sequences without fat suppression acquired on a 3 T MR scanner were retrospectively analyzed in 10 healthy volunteers and 42 patients suffering from clinically and electrophysiologically diagnosed sciatic neuropathy. A fully-convolutional neural network was developed to segment the MRN images into peripheral nerve and background tissues. The performance of the method was compared to manual inter-rater segmentation variability. The proposed method yielded Dice coefficients of 0.859 ± 0.061 and 0.719 ± 0.128, Hausdorff distances of 13.9 ± 26.6 and 12.4 ± 12.1 mm, and volumetric similarities of 0.930 ± 0.054 and 0.897 ± 0.109, for the healthy volunteer and patient cohorts, respectively. The complete segmentation process requires less than one second, which is a significant decrease to manual segmentation with an average duration of 19 ± 8 min. Considering cross-sectional area or signal intensity of the segmented nerves, focal and extended lesions might be detected. Such analyses could be used as biomarker for lesion burden, or serve as volume of interest for further quantitative MRN techniques. We demonstrated that fully-automatic segmentation of healthy and neuropathic sciatic nerves can be performed from standard MRN images with good accuracy and in a clinically feasible time.
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