Abstract
Automatic detection and identification of the intervertebral discs on the spine MR images is a challenging task due to similarity of the discs on the same image, size and shape differences between subjects, and poor resolution. Many deep learning-based methods have been proposed recently to achieve automated detection and identification of human intervertebral discs. However, since there is usually only a small amount of labeled vertebral images available, employing an end-to-end deep learning system is not easily achievable. In this paper, we use a multi-stage deep learning system to detect and identify human lumbar discs from MRI data. We first use a Faster Region based Convolutional Neural Network (FRCNN) method to detect candidate disc positions. Each candidate from the FRCNN becomes a node in a weighted graph structure. The edge weights between the nodes are calculated using the FRCNN scores and the scores from a Binary Classifier Network (BCN) that tests compatibility of the nodes of the edge. A novel application of Dijkstra’s shortest path algorithm in this network produces both localizations and identifications of the lumbar discs in a globally optimal manner. Experiments on our dataset of 80 MRI scans from 80 patients achieved very promising results as they exceeded the state of the art alternatives on similar datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, Y., Landis, M., Laidley, D.T., Kornecki, A., Lum, A., Li, S.: Multi-modal vertebrae recognition using transformed deep convolution network. Comput. Med. Imag. Graph. 51, 11–19 (2016)
Chen, H., et al.: Automatic localization and identification of vertebrae in spine CT via a joint learning model with deep neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 515–522. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24553-9_63
Forsberg, D., Sjöblom, E., Sunshine, J.L.: Detection and labeling of vertebrae in mr images using deep learning with clinical annotations as training data. J. Dig. Imag. 30(4), 406–412 (2017)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)
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)
Glasmachers, T.: Limits of end-to-end learning. In: Asian Conference on Machine Learning, pp. 17–32 (2017)
Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_73
Jamaludin, A., Lootus, M., Kadir, T., Zisserman, A.: Automatic intervertebral discs localization and segmentation: a vertebral approach. In: Vrtovec, T., et al. (eds.) CSI 2015. LNCS, vol. 9402, pp. 97–103. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41827-8_9
Karakoç, N.S., Karahan, Ş., Akgül, Y.S.: Deep learning based estimation of the eye pupil center by using image patch classification. In: 2017 25th Signal Processing and Communications Applications Conference (SIU), pp. 1–4. IEEE (2017)
Lootus, M., Kadir, T., Zisserman, A.: Vertebrae detection and labelling in lumbar MR images. In: Yao, J., Klinder, T., Li, S. (eds.) Computational Methods and Clinical Applications for Spine Imaging. LNCVB, vol. 17, pp. 219–230. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07269-2_19
Oktay, A.B., Akgul, Y.S.: Simultaneous localization of lumbar vertebrae and intervertebral discs with svm-based mrf. IEEE Trans. Biomed. Eng. 60(9), 2375–2383 (2013)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)
Suzani, A., Seitel, A., Liu, Y., Fels, S., Rohling, R.N., Abolmaesumi, P.: Fast automatic vertebrae detection and localization in pathological CT Scans - a deep learning approach. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 678–686. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_81
Wang, X., Zhai, S., Niu, Y.: Automatic vertebrae localization and identification by combining deep SSAE contextual features and structured regression forest. J. Dig. Imag. 32, 1–13 (2019). https://doi.org/10.1007/s10278-018-0140-5
Yang, D., et al.: Automatic vertebra labeling in large-scale 3D CT using deep image-to-image network with message passing and sparsity regularization. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 633–644. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59050-9_50
Zukić, D., Vlasák, A., Egger, J., Hořínek, D., Nimsky, C., Kolb, A.: Robust detection and segmentation for diagnosis of vertebral diseases using routine MR images. In: Computer Graphics Forum, vol. 33, pp. 190–204. Wiley Online Library (2014)
Acknowledgement
We would like to thank Dr. Ayse Betul Oktay for providing the dataset and also TUBITAK-BILGEM Cloud Computing and Big Data Laboratory (B3LAB) for allowing us to use their GPU servers.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zeybel, M., Akgul, Y.S. (2020). Localization and Identification of Lumbar Intervertebral Discs on Spine MR Images with Faster RCNN Based Shortest Path Algorithm. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-52791-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52790-7
Online ISBN: 978-3-030-52791-4
eBook Packages: Computer ScienceComputer Science (R0)