A high-quality dataset featuring classified and annotated cervical spine X-ray atlas.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
13 Jun 2024
13 Jun 2024
Historique:
received:
23
01
2024
accepted:
15
05
2024
medline:
14
6
2024
pubmed:
14
6
2024
entrez:
13
6
2024
Statut:
epublish
Résumé
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image recognition in the medical field, which requires large-scale and high-quality training datasets consisting of raw images and annotated images. However, suitable experimental datasets for cervical spine X-ray are scarce. We fill the gap by providing an open-access Cervical Spine X-ray Atlas (CSXA), which includes 4963 raw PNG images and 4963 annotated images with JSON format (JavaScript Object Notation). Every image in the CSXA is enriched with gender, age, pixel equivalent, asymptomatic and symptomatic classifications, cervical curvature categorization and 118 quantitative parameters. Subsequently, an efficient algorithm has developed to transform 23 keypoints in images into 77 quantitative parameters for cervical spine disease diagnosis and treatment. The algorithm's development is intended to assist future researchers in repurposing annotated images for the advancement of machine learning techniques across various image recognition tasks. The CSXA and algorithm are open-access with the intention of aiding the research communities in experiment replication and advancing the field of medical imaging in cervical spine.
Identifiants
pubmed: 38871800
doi: 10.1038/s41597-024-03383-0
pii: 10.1038/s41597-024-03383-0
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
625Informations de copyright
© 2024. The Author(s).
Références
Le Huec, J. C., Thompson, W., Mohsinaly, Y., Barrey, C. & Faundez, A. Sagittal balance of the spine. Eur Spine J. 28, 1889–1905 (2019).
doi: 10.1007/s00586-019-06083-1
pubmed: 31332569
Xu, C., Lin, B., Ding, Z. & Xu, Y. Cervical degenerative spondylolisthesis: analysis of facet orientation and the severity of cervical spondylolisthesis. Spine J. 16, 10–5 (2016).
doi: 10.1016/j.spinee.2015.09.035
pubmed: 26409420
Hurwitz, E. L., Randhawa, K., Yu, H., Côté, P. & Haldeman, S. The Global Spine Care Initiative: a summary of the global burden of low back and neck pain studies. Eur Spine J. 27, 796–801 (2018).
doi: 10.1007/s00586-017-5432-9
pubmed: 29480409
Luckhurst, C. M. et al. Pediatric Cervical Spine Injury Following Blunt Trauma in Children Younger Than 3 Years: The PEDSPINE II Study. JAMA Surg. 158, 1126–1132 (2023).
doi: 10.1001/jamasurg.2023.4213
pubmed: 37703025
Theodore, N. Degenerative cervical spondylosis. N Engl J Med. 383, 159–168 (2020).
doi: 10.1056/NEJMra2003558
pubmed: 32640134
Oliver, J. D. et al. Comparison of Outcomes for Anterior Cervical Discectomy and Fusion With and Without Anterior Plate Fixation: A Systematic Review and Meta-Analysis. Spine (Phila Pa 1976). 43, E413–E422 (2018).
doi: 10.1097/BRS.0000000000002441
pubmed: 29016435
Ren, G. et al. CurrentApplications of Machine Learning in Spine: From Clinical View. Global Spine J. 12, 1827–1840 (2022).
doi: 10.1177/21925682211035363
pubmed: 34628966
Freund, Y. et al. Effect of Systematic Physician Cross-checking on Reducing Adverse Events in the Emergency Department: The CHARMED Cluster Randomized Trial. JAMA Intern Med. 178, 812–819 (2018).
doi: 10.1001/jamainternmed.2018.0607
pubmed: 29710111
pmcid: 6145759
Johnson, A. E. W. et al. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6, 317 (2019).
doi: 10.1038/s41597-019-0322-0
pubmed: 31831740
pmcid: 6908718
Der Sarkissian, H. et al. A cone-beam X-ray computed tomography data collection designed for machine learning. Sci Data 6, 215 (2019).
doi: 10.1038/s41597-019-0235-y
Pham, H. H. et al. PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children. Sci Data 10, 240 (2023).
doi: 10.1038/s41597-023-02102-5
pubmed: 37100784
pmcid: 10133237
Nguyen, H. Q. et al. VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. Sci Data 9, 429 (2022).
doi: 10.1038/s41597-022-01498-w
pubmed: 35858929
pmcid: 9300612
Rutherford, M. et al. A DICOM dataset for evaluation of medical image de-identification. Sci Data 8, 183 (2021).
doi: 10.1038/s41597-021-00967-y
pubmed: 34272388
pmcid: 8285420
Abedeen, I. et al. FracAtlas: A Dataset for Fracture Classification, Localization and Segmentation of Musculoskeletal Radiographs. Sci Data 10, 521 (2023).
doi: 10.1038/s41597-023-02432-4
pubmed: 37543626
pmcid: 10404222
Scheer, J. K., Lau, D., Ames, C. P. Sagittal balance of the cervical spine. J Orthop Surg (Hong Kong). 29(1_suppl) (2021).
Zheng, H. D. et al. Deep learning-based high-accuracy quantitation for lumbar intervertebral disc degeneration from MRI. Nat Commun. 13, 841 (2022).
doi: 10.1038/s41467-022-28387-5
pubmed: 35149684
pmcid: 8837609
Wang, C. et al. Deep learning model for measuring the sagittal Cobb angle on cervical spine computed tomography. BMC Med Imaging. 23, 196 (2023).
doi: 10.1186/s12880-023-01156-6
pubmed: 38017414
pmcid: 10685593
Rababaah, A. R., Demi-Ejegi, Y. Automatic visual inspection system for stamped sheet metals (AVIS3M). 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Zhangjiajie, China, 2012, pp. 661–665.
Ohara, A., Miyamoto, K., Naganawa, T., Matsumoto, K. & Shimizu, K. Reliabilities of and correlations among five standard methods of assessing the sagittal alignment of the cervical spine. Spine (Phila Pa 1976). 31, 2585–91 (2006).
doi: 10.1097/01.brs.0000240656.79060.18
pubmed: 17047548
Chen, X., Sima, S., Sandhu, H. S., Kuan, J. & Diwan, A. D. Radiographic evaluation of lumbar intervertebral disc height index: An intra and inter-rater agreement and reliability study. J Clin Neurosci. 103, 153–162 (2022).
doi: 10.1016/j.jocn.2022.07.018
pubmed: 35905524
Huang, Z., Zhu, Y. & Yuan, W. Correlation Between Parameters of Intervertebral Disc and Cervical Lordosis in Cervical Spondylotic Myelopathy. Med Sci Monit. 17, e924857 (2020).
Hedlund, J., Ekström, L. & Thoreson, O. Porcine Functional Spine Unit in orthopedic research, a systematic scoping review of the methodology. J Exp Orthop. 9, 54 (2022).
doi: 10.1186/s40634-022-00488-6
pubmed: 35678892
pmcid: 9184692
Rueangsri, C., Puntumetakul, R., Leungbootnak, A., Sae-Jung, S. & Chatprem, T. Cervical Spine Instability Screening Tool Thai Version: Assessment of Convergent Validity and Rater Reliability. Int J Environ Res Public Health. 25, 6645 (2023).
doi: 10.3390/ijerph20176645
Protopsaltis, T. S. et al. The Importance of C2 Slope, a Singular Marker of Cervical Deformity, Correlates With Patient-reported Outcomes. Spine (Phila Pa 1976). 45, 184–192 (2020).
doi: 10.1097/BRS.0000000000003214
pubmed: 31513111
Qi, C. et al. Does cervical curvature affect neurological outcome after incomplete spinal cord injury without radiographic abnormality (SCIWORA): 1-year follow-up. J Orthop Surg Res. 17, 361 (2022).
doi: 10.1186/s13018-022-03254-7
pubmed: 35883148
pmcid: 9327310
Zhang, J., Buser, Z., Abedi, A., Dong, X. & Wang, J. C. Can C2-6 Cobb Angle Replace C2-7 Cobb Angle? An Analysis of Cervical Kinetic Magnetic Resonance Images and X-rays. Spine (Phila Pa 1976). 44, 240–245 (2019).
doi: 10.1097/BRS.0000000000002795
pubmed: 30015714
Ran, Y. et al. Cervical Spine X-ray Atlas (CSXA) V3.0, V1. Science Data Bank https://doi.org/10.57760/sciencedb.15391 (2024).