Abstract
The identification and detection of degenerative osteophytes of the spine is a challenging and time-consuming task that is important for the diagnosis of many spine diseases. Previous attempts to automate this task have been focused on using image features derived from radiographic diagnostic expertise rather than directly learning features. In this paper, we present a bottom-up approach to generate features for classification using a region-based convolutional neural network with unwrapped cortical shell maps from 18F-NaF positron emission tomography and computed tomography scans of the vertebral bodies of the thoracic and lumbar spine. We evaluated osteophyte detection performance on 45 individuals with 5-fold cross validation and achieved state-of-the-art performance with 85% sensitivity at 2 false positive detections per patient.
The rights of this work are transferred to the extent transferable according to title 17 §105 U.S.C.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
References
Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., Greenspan, H.: Chest pathology detection using deep learning with non-medical training. In: IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015, pp. 294–297. IEEE (2015)
Bastawrous, S., Bhargava, P., Behnia, F., Djang, D.S., Haseley, D.R.: Newer pet application with an old tracer: role of 18F-NaF skeletal PET/CT in oncologic practice. Radiographics 34(5), 1295–1316 (2014)
Brown, M.S., Chu, G.H., Kim, H.J., Allen-Auerbach, M., Poon, C., Bridges, J., Vidovic, A., Ramakrishna, B., Ho, J., Morris, M.J., et al.: Computer-aided quantitative bone scan assessment of prostate cancer treatment response. Nuclear Med. Commun. 33(4), 384 (2012)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of the ACM International Conference on Multimedia, pp. 675–678. ACM (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Liu, J., Lay, N., Wei, Z., Lu, L., Kim, L., Turkbey, E., Summers, R.M.: Colitis detection on abdominal CT scans by rich feature hierarchies. In: SPIE Medical Imaging, p. 97851N. International Society for Optics and Photonics (2016)
Nathan, H.: Osteophytes of the vertebral column. J. Bone Joint Surg. Am. 44(2), 243–268 (1962)
Resnick, D.: Degenerative diseases of the vertebral column. Radiology 156(1), 3–14 (1985)
Tan, S., Yao, J., Ward, M.M., Yao, L., Summers, R.M.: Computer aided evaluation of ankylosing spondylitis. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 339–342. IEEE (2006)
Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. Int. J. Comput. Vis. 104(2), 154–171 (2013)
Yao, J., Burns, J.E., Munoz, H., Summers, R.M.: Detection of vertebral body fractures based on cortical shell unwrapping. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 509–516. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33454-2_63
Yao, J., Munoz, H., Burns, J.E., Lu, L., Summers, R.M.: Computer aided detection of spinal degenerative osteophytes on Sodium Fluoride PET/CT. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 17, pp. 51–60. Springer, Cham (2014). doi:10.1007/978-3-319-07269-2_5
Yao, J., O’Connor, S.D., Summers, R.M.: Automated spinal column extraction and partitioning. In: 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, pp. 390–393. IEEE (2006)
Acknowledgments
This research was supported in part by the Intramural Research Program of National Institutes of Health Clinical Center.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG (outside the US)
About this paper
Cite this paper
Wang, Y., Yao, J., Burns, J.E., Liu, J., Summers, R.M. (2016). Detection of Degenerative Osteophytes of the Spine on PET/CT Using Region-Based Convolutional Neural Networks. In: Yao, J., Vrtovec, T., Zheng, G., Frangi, A., Glocker, B., Li, S. (eds) Computational Methods and Clinical Applications for Spine Imaging. CSI 2016. Lecture Notes in Computer Science(), vol 10182. Springer, Cham. https://doi.org/10.1007/978-3-319-55050-3_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-55050-3_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55049-7
Online ISBN: 978-3-319-55050-3
eBook Packages: Computer ScienceComputer Science (R0)