ArticleJournal of applied clinical medical physics2024
A quantitative evaluation of the deep learning model of segmentation and measurement of cervical spine MRI in healthy adults.
Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 10 citations in OpenAlex.
- 3D U-net for volumetric segmentation of spinal cord MRI in degenerative disorders.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- A two-stage deep learning system for cervical spine fracture diagnosis: integrating 3D segmentation and 2.5D classification on CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Joint segmentation and classification of cervical cells using U-Net and CNN ensemble for early detection of cervical cancer.Cancer cell international · 2026Article
- Structure-aware multi-task learning with domain generalization for robust vertebrae analysis in spinal CT.NPJ digital medicine · 2026Article
- The Duke University Cervical Spine MRI Segmentation Dataset (CSpineSeg).Scientific data · 2025Article
- Applications of Artificial Intelligence and Machine Learning in Spine MRI.Bioengineering (Basel, Switzerland) · 2024Review
- MRI-Derived Dural Sac and Lumbar Vertebrae 3D Volumetry Has Potential for Detection of Marfan Syndrome.Diagnostics (Basel, Switzerland) · 2024Article
- A quantitative evaluation of the deep learning model of segmentation and measurement of cervical spine MRI in healthy adults.Journal of applied clinical medical physics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo evaluate the 3D U-Net model for automatic segmentation and measurement of cervical spine structures using magnetic resonance (MR) images of healthy adults. MATERIALS AND
methodsMR images of the cervical spine from 160 healthy adults were collected retrospectively. A previously constructed deep-learning model was used to automatically segment anatomical structures. Segmentation and localization results were checked by experienced radiologists. Pearson's correlation analyses were conducted to examine relationships between patient and image parameters.
resultsNo measurement was significantly correlated with age or sex. The mean values of the areas of the subarachnoid space and spinal cord from the C2/3 (cervical spine 2-3) to C6/7 intervertebral disc levels were 102.85-358.12 mm
conclusionA deep learning model based on 3D U-Net automatically segmented and performed measurements on cervical spine MR images from healthy adults, paving the way for quantitative diagnosis models for spinal cord diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.