ArticleJournal of digital imaging2017
Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- 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
- Automated detection, labelling and radiological grading of clinical spinal MRIs.Scientific reports · 2024Article
- Research on Three-Dimensional Reconstruction of Ribs Based on Point Cloud Adaptive Smoothing Denoising.Sensors (Basel, Switzerland) · 2024Article
- A fully automatic MRI-guided decision support system for lumbar disc herniation using machine learning.JOR spine · 2024Article
- Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.Journal of medical systems · 2023Article
- Automatic segmentation and radiomic texture analysis for osteoporosis screening using chest low-dose computed tomography.European radiology · 2023Article
- Research and application progress in deep learning in otology.Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2023Article
- Artificial Intelligence in Spinal Imaging: Current Status and Future Directions.International journal of environmental research and public health · 2022Review
- Current development and prospects of deep learning in spine image analysis: a literature review.Quantitative imaging in medicine and surgery · 2022Review
- Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database.Scientific data · 2022Article
- Arterial enhancing local tumor progression detection on CT images using convolutional neural network after hepatocellular carcinoma ablation: a preliminary study.Scientific reports · 2022Article
- Machine learning for image analysis in the cervical spine: Systematic review of the available models and methods.Brain & spine · 2022Review
- Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning.Medical image analysis · 2021Article
- Machine learning in orthopaedic surgery.World journal of orthopedics · 2021Review
- Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging.Radiology. Artificial intelligence · 2021Review
- Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs.Scientific reports · 2021Article
- Deep learning for the rapid automatic quantification and characterization of rotator cuff muscle degeneration from shoulder CT datasets.European radiology · 2021Article
- Lumbar Disc Herniation Automatic Detection in Magnetic Resonance Imaging Based on Deep Learning.Frontiers in bioengineering and biotechnology · 2021Article
- Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.Biomedical engineering online · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to investigate the potential of using clinically provided spine label annotations stored in a single institution image archive as training data for deep learning-based vertebral detection and labeling pipelines. Lumbar and cervical magnetic resonance imaging cases with annotated spine labels were identified and exported from an image archive. Two separate pipelines were configured and trained for lumbar and cervical cases respectively, using the same setup with convolutional neural networks for detection and parts-based graphical models to label the vertebrae. The detection sensitivity, precision and accuracy rates ranged between 99.1-99.8, 99.6-100, and 98.8-99.8% respectively, the average localization error ranges were 1.18-1.24 and 2.38-2.60 mm for cervical and lumbar cases respectively, and with a labeling accuracy of 96.0-97.0%. Failed labeling results typically involved failed S1 detections or missed vertebrae that were not fully visible on the image. These results show that clinically annotated image data from one image archive is sufficient to train a deep learning-based pipeline for accurate detection and labeling of MR images depicting the spine. Further, these results support using deep learning to assist radiologists in their work by providing highly accurate labels that only require rapid confirmation.
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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.