Abstract
Purpose Lower back pain affects 80–90 % of all people at some point during their life time, and it is considered as the second most neurological ailment after headache. It is caused by defects in the discs, vertebrae, or the soft tissues. Radiologists perform diagnosis mainly from X-ray radiographs, MRI, or CT depending on the target organ. Vertebra fracture is usually diagnosed from X-ray radiographs or CT depending on the available technology. In this paper, we propose a fully automated Computer-Aided Diagnosis System (CAD) for the diagnosis of vertebra wedge compression fracture from CT images that integrates within the clinical routine.
Methods We perform vertebrae localization and labeling, segment the vertebrae, and then diagnose each vertebra. We perform labeling and segmentation via coordinated system that consists of an Active Shape Model and a Gradient Vector Flow Active Contours (GVF-Snake). We propose a set of clinically motivated features that distinguish the fractured vertebra. We provide two machine learning solutions that utilize our features including a supervised learner (Neural Networks (NN)) and an unsupervised learner (K-Means).
Results We validate our method on a set of fifty (thirty abnormal) Computed Tomography (CT) cases obtained from our collaborating radiology center. Our diagnosis detection accuracy using NN is 93.2 % on average while we obtained 98 % diagnosis accuracy using K-Means. Our K-Means resulted in a specificity of 87.5 % and sensitivity over 99 %.
Conclusions We presented a fully automated CAD system that seamlessly integrates within the clinical work flow of the radiologist. Our clinically motivated features resulted in a great performance of both the supervised and unsupervised learners that we utilize to validate our CAD system. Our CAD system results are promising to serve in clinical applications after extensive validation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Al-Helo S, Alomari R, Chaudhary V, Al-Zoubi M (2011) Segmentation of lumbar vertebrae from clinical CT using active shape models and GVF-snake. In: Proceedings international IEEE conference in engineering in medicine and biology society (EMBC’11), pp 8033–8036. IEEE, Boston, MA, USA
Alomari R, Corso J, Chaudhary V (2011) Labeling of lumbar discs using both pixel- and object-level features with a two level probabilistic model. IEEE Trans Med Imaging 30(1):1–10
Alomari R, Corso J, Chaudhary V, Dhillon G (2011) Toward a clinical lumbar CAD: Herniation diagnosis. Int J Comput Assist Radiol Surg (IJCARS) 6(1):119–126
Bishop C (2006) Pattern recognition and machine learning (Information Science and Statistics). Springer, New York
Cherukuri M, Stanley R, Long R, Antani S, Thoma G (2004) Anterior osteophyte discrimination in lumbar vertebrae using size invariant features. Comput Med Imaging Graph 28(1–2):99–108
Cootes TF, Taylor CJ (2001) Statistical models of appearance for medical image analysis and computer vision. In: Sonka M, Hanson KM (eds) Proceedings of SPIE medical imaging, p 236
Eastell R, Cedel S, Wahner H, Riggs B, Melton L (1991) Classification of vertebral fractures. J Bone Miner Res 6(3):207–215
eOrthopod: Image used courtesy of medical multimedia group, llc. more information is available at eorthopod.com. http://www.eorthopod.com/content/adult-lumbar-spine-fractures-types. Accessed September 2012
Ghosh S, Alomari R, Chaudhary V, Dhillon G (2011) Automatic lumbar vertebra segmentation from clinical CT for wedge compression fracture diagnosis. In: Proceedings SPIE medical imaging, vol 7963, p 796303. Orlando, Florida, USA
Kasai S, Li F, Shiraishi J, Li Q, Doi K (2006) Computerized detection of vertebral compression fractures on lateral chest radiographs: preliminary results with a tool for early detection of osteoporosis. Med Phys 33(12):4664–4674
Mastmeyer A, Engelke K, Fuchs C, Kalender W (2006) A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Med Image Anal 10(4):560–577
Roberts M, Cootes T, Pacheco E, Adams J (2007) Quantitative vertebral fracture detection on DXA images using shape and appearance models. Acad Radiol 14(10):1166–1178
Smyth P, Taylor C, Adams J (1999) Vertebral shape: automatic measurement with active shape models. Radiology 211(2): 571–578
Spine Universe: http://www.spineuniverse.com/. Accessed September 2012
Tan S, Yao J, Ward M, Yao L, Summers R (2008) Computer aided evaluation of ankylosing spondylitis using high-resolution CT. IEEE Trans Med Imaging 27(9):1252–1267
T.F Cootes D Copper CT, Graham J (1995) Active shape models—their training and application. Comput Vis Image Underst 61(1):38–59
Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369
Acknowledgments
Thisworkwas funded in part by grants from NSF MRI-R2, NSF CRI, and UB CAT.
Conflict of interest
None.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Al-Helo, S., Alomari, R.S., Ghosh, S. et al. Compression fracture diagnosis in lumbar: a clinical CAD system. Int J CARS 8, 461–469 (2013). https://doi.org/10.1007/s11548-012-0796-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-012-0796-0