A Vertebral Segmentation Dataset with Fracture Grading
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Keywords: CT, Computer Aided Diagnosis (CAD), Computer Applications-General (Informatics), Convolutional Neural Network (CNN), Diagnosis, Neural Networks, Perceptron, Segmentation, Skeletal-Axial, Spine, Supervised learning Published under a CC BY 4.0 license. Supplemental material is available for this article.
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A framework for fully automatic segmentation of vertebrae in any CT dataset within several seconds was recently introduced.
“We recently introduced a framework for fully automatic segmentation of vertebrae in any CT dataset within several seconds [16, 17].”
From Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA (2021) · cites this paper as [16]
The Materials and Methods section (Vertebral Segmentation; page unavailable) describes a framework that predicts vertebral segmentations from CT images. It does not establish that the framework works on any CT dataset or completes segmentation within several seconds, and the dataset’s final masks were manually refined. (LLM, conf=0.87)
Experimental — accuracy not yet measuredVertebrae were automatically segmented in CT scans using a framework of CNNs that identifies the spine, labels each vertebral body, and creates segmentation masks.
“First, vertebrae were automatically segmented in CT scans using a framework of CNNs that identifies the spine, labels each vertebral body, and creates segmentation masks [16].”
From Automatic opportunistic osteoporosis screening in routine CT: improved prediction of patients with prevalent vertebral fractures compared to DXA (2021) · cites this paper as [16]
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Yushkevich, P. A., Piven, J., Hazlett, H. C., Smith, R. G., Ho, S., Gee, J. C., & Gerig, G. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.015 · Cited by 8,868 · see citing works
McDonald, R. J., Schwartz, K. M., Eckel, L. J., Diehn, F. E., Hunt, C. H., Bartholmai, B. J., Erickson, B. J., & Kallmes, D. F. (2015) · Academic Radiology · doi:10.1016/j.acra.2015.05.007 · Cited by 460 · see citing works
Flagged first, then by citations
Research works printed but not linked: 26
- [1]Sekuboyina A, Kukačka J, Kirschke JS, Menze BH, Valentinitsch A. Attention-Driven Deep Learning for Pathological Spine Segmentation. In: Glocker B, Yao J, Vrtovec T, Frangi A, Zheng G, eds. Computational Methods and Clinical Applications in Musculoskeletal Imaging. MSKI 2017. Lecture Notes in Computer Science, vol 10734. Cham, Switzerland: Springer, 2018; 108 – 119.
- [2]Yao J, Burns JE, Munoz H, Summers RM. Detection of Vertebral Body Fractures Based on Cortical Shell Unwrapping. In: Ayache N, Delingette H, Golland P, Mori K, eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7512. Berlin, Germany: Springer, 2012; 509 – 516. PMC3935239 23286169
- [3]Baum T, Bauer JS, Klinder T, et al. Automatic detection of osteoporotic vertebral fractures in routine thoracic and abdominal MDCT. Eur Radiol 2014; 24 (4): 872 – 880. 24425527
- [4]Burns JE, Yao J, Muñoz H, Summers RM. Automated Detection, Localization, and Classification of Traumatic Vertebral Body Fractures in the Thoracic and Lumbar Spine at CT. Radiology 2016; 278 (1): 64 – 73. 26172532 PMC4699497
- [5]Burns JE, Yao J, Summers RM. Vertebral Body Compression Fractures and Bone Density: Automated Detection and Classification on CT Images. Radiology 2017; 284 (3): 788 – 797. 28301777 PMC5584647
- [6]Valentinitsch A, Trebeschi S, Alarcón E, et al. Regional analysis of age-related local bone loss in the spine of a healthy population using 3D voxel-based modeling. Bone 2017; 103: 233 – 240. 28716553
- [7]Pickhardt PJ, Lee SJ, Liu J, et al. Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes. Br J Radiol 2019; 92 (1094): 20180726. 30433815 PMC6404831
- [8]Williams AL, Al-Busaidi A, Sparrow PJ, Adams JE, Whitehouse RW. Under-reporting of osteoporotic vertebral fractures on computed tomography. Eur J Radiol 2009; 69 (1): 179 – 183. 17913429
- [10]Sim Y, Chung MJ, Kotter E, et al. Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs. Radiology 2020; 294 (1): 199 – 209. 31714194
- [11]Kickingereder P, Isensee F, Tursunova I, et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 2019; 20 (5): 728 – 740. 30952559
- [12]Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005; 26 (3): 839 – 851. 15955494
- [13]Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C. Automated model-based vertebra detection, identification, and segmentation in CT images. Med Image Anal 2009; 13 (3): 471 – 482. 19285910
- [14]Lessmann N, van Ginneken B, de Jong PA, Išgum I. Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 2019; 53: 142 – 155. 30771712
- [15]Janssens R, Zeng G, Zheng G. Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018; 893 – 897.
- [16]Sekuboyina A, Bayat A, Husseini ME, et al. VerSe: A Vertebrae Labelling and Segmentation Benchmark. arXiv:200109193 [cs, eess] [preprint]. http://arxiv.org/abs/2001.09193. Posted 2020. Accessed February 24, 2020. 34340104 · arXiv
- [17]Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer 2018; 18 (8): 500 – 510. 29777175 PMC6268174
- [18]Yao J, Burns JE, Forsberg D, et al. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph 2016; 49: 16 – 28. 26878138 PMC5527557
- [19]Korez R, Ibragimov B, Likar B, Pernuš F, Vrtovec T. A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Trans Med Imaging 2015; 34 (8): 1649 – 1662. 25585415
- [20]Ibragimov B, Likar B, Pernuš F, Vrtovec T. Shape representation for efficient landmark-based segmentation in 3-d. IEEE Trans Med Imaging 2014; 33 (4): 861 – 874. 24710155
- [21]Löffler MT, Jacob A, Valentinitsch A, et al. Improved prediction of incident vertebral fractures using opportunistic QCT compared to DXA. Eur Radiol 2019; 29 (9): 4980 – 4989. 30790025 PMC6682570
- [22]Sekuboyina A, Rempfler M, Valentinitsch A, Menze BH, Kirschke JS. Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy. Radiol Artif Intell 2020; 2 (2): e190074. PMC8017405 33937818
- [23]Sekuboyina A, Rempfler M, Kukačka J, et al. Btrfly Net: Vertebrae Labelling with Energy-based Adversarial Learning of Local Spine Prior. arXiv:180401307 [cs] [preprint]. http://arxiv.org/abs/1804.01307. Posted 2018. Accessed August 1, 2019. · arXiv
- [26]Genant HK, Wu CY, van Kuijk C, Nevitt MC. Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 1993; 8 (9): 1137 – 1148. 8237484
- [27]Löffler MT, Sollmann N, Mei K, et al. X-ray-based quantitative osteoporosis imaging at the spine. Osteoporos Int 2020; 31 (2): 233 – 250. 31728606
- [28]Rajapakse CS, Phillips EA, Sun W, et al. Vertebral deformities and fractures are associated with MRI and pQCT measures obtained at the distal tibia and radius of postmenopausal women. Osteoporos Int 2014; 25 (3): 973 – 982. 24221453 PMC4746757
- [29]Payer C, Štern D, Bischof H, Urschler M. Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net. In: Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, Valletta, Malta, February 27–29, 2020. Setúbal, Portugal: Science and Technology Publications, 2020; 124 – 133.
Cited by
Sorted by citations
Segment anything model for medical images?
Huang, Y., Yang, X., Liu, L., Zhou, H., Chang, A., Zhou, X., Chen, R., Yu, J., Chen, J., Chen, C., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103061 · Cited by 536 · see citing works
“Additionally, the original version of SAM only supported 2D input, and 2D format is fundamental and even a basic component for 3D / 4D format.”
Löffler, M. T., Jacob, A., Scharr, A., Sollmann, N., Burian, E., El Husseini, M., Sekuboyina, A., Tetteh, G., Zimmer, C., Gempt, J., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07655-2 · Cited by 120 · see citing works
“We recently introduced a framework for fully automatic segmentation of vertebrae in any CT dataset within several seconds [16, 17].”
MethodSelf-citationSAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images
Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2310.15161 · Cited by 115
Abumoussa, A., Gopalakrishnan, V., Succop, B., Galgano, M., Jaikumar, S., Lee, Y. Z., & Bhowmick, D. A. (2023) · Neurosurgical Focus · doi:10.3171/2023.3.focus2345 · Cited by 9 · see citing works
- Authors
- Löffler, M. T., Sekuboyina, A., Jacob, A., Grau, A.-L., Scharr, A., El Husseini, M., Kallweit, M., Zimmer, C., Baum, T., & Kirschke, J. S.
- Affiliations
- TUM Klinikum (DE)
- From the Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Ismaninger Str 22, Munich 81675, Germany (M.T.L., A. Sekuboyina, A.J., A.L.G., A. Scharr, M.E.H., M.K., C.Z., T.B., J.S.K.); and Department of Informatics, Technical University of Munich, Munich, Germany (A. Sekuboyina).
- Corresponding author
- Löffler, Maximilian T.
- ORCID
- Venue
- Radiology: Artificial Intelligence
- Published
- 2020-07-01
- Volume
- 2
- Issue
- 4
- Pages
- e190138
- ISSN
- 2638-6100 (ISSN-L)
- PMC
- PMC8082364
- OpenAlex
- W3046053509
- Cited by
- 181
- References
- 3
- Citations by year
- 2026: 21 · 2025: 34 · 2024: 41 · 2023: 27 · 2022: 29 · 2021: 23 · 2020: 5
- FWCI
- 6.60
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-sa
- Locations
- Radiology Artificial Intelligence (publishedVersion, cc-by-sa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- European Research Council (637164)
- Deutsche Forschungsgemeinschaft (432290010)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- vertebral segmentation, spine, CT, computer-aided diagnosis, convolutional neural networks, fracture detection
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