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Erschienen in:
Segmentation of Vertebrae from 3D Spine Images by Applying Concepts from Transportation and Game Theories Kapitel lesen Erstes Kapitel lesen

2014 | OriginalPaper | Buchkapitel

Vertebrae Detection and Labelling in Lumbar MR Images

verfasst von : Meelis Lootus, Timor Kadir, Andrew Zisserman

Erschienen in: Computational Methods and Clinical Applications for Spine Imaging

Verlag: Springer International Publishing

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Abstract

We describe a method to automatically detect and label the vertebrae in human lumbar spine MRI scans. Our contribution is to show that marrying two strong algorithms (the DPM object detector of Felzenszwalb et al. [1], and inference using dynamic programming on chains) together with appropriate modelling, results in a simple, computationally cheap procedure, that achieves state-of-the-art performance. The training of the algorithm is principled, and heuristics are not required. The method is evaluated quantitatively on a dataset of 371 MRI scans, and it is shown that the method copes with pathologies such as scoliosis, joined vertebrae, deformed vertebrae and disks, and imaging artifacts. We also demonstrate that the same method is applicable (without retraining) to CT scans.

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Vorheriges Kapitel Registration of MR to Percutaneous Ultrasound of the Spine for Image-Guided Surgery
Literatur
1.
Felzenszwalb, P., Mcallester, D., Ramanan, D.: A discriminatively trained, multiscale, deformable part model. In: Proceedings of CVPR (2008)
2.
Pfirmann, C.W.A., Metzdorf, A., Zanetti, M., Hodler, J., Boos, N.: Magnetic resonance classification of lumbar intervertebral disc degeneration. Spine 26(17), 1873–1878 (2001)CrossRef
3.
Fardon, D.F., Milette, P.C.: Nomenclature and classification of lumbar disc pathology. Spine 26(5), E93–E113 (2001)CrossRef
4.
Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Desiccation diagnosis in lumbar discs from clinical mri with a probabilistic model. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro. ISBI ’09, pp. 546–549 (2009)
5.
Alomari, R.S., Corso, J.J., Chaudhary, V., Dhillon, G.: Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI. Int. J. Comput. Assist. Radiol. Surg. 5(3), 287–293 (2010)CrossRef
6.
Ghosh, S., Alomari, R.S., Chaudhary, V., Dhillon, G.: Computer-aided diagnosis for lumbar mri using heterogeneous classifiers. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro (2011)
7.
Michopoulou, S., Costaridou, L., Vlychou, M., Speller, R., Todd-Pokropek, A.: Texture-based quantification of lumbar intervertebral disc degeneration from conventional t2-weighted MRI. Acta Radiol. 52(1), 91–98 (2011)CrossRef
8.
Ghosh, S., Alomari, R.S., Chaudhary, V., Dhillon, G.: Automatic lumbar vertebra segmentation from clinical ct for wedge compression fracture diagnosis. In: SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis (2011)
9.
Wels, M., Kelm, B.M., Tsymbal, A., Hammon, M., Soza, G., Sühling, M., Cavallaro, A., Comaniciu, D.: Multi-stage osteolytic spinal bone lesion detection from ct data with internal sensitivity control. In: Proceedings of SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis (2012)
10.
Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: Proceedings of CVPR, vol. 2, pp. 886–893 (2005)
11.
Fischler, M., Elschlager, R.: The representation and matching of pictorial structures. IEEE Trans. Comput. c–22(1), 67–92 (1973)CrossRef
12.
Felzenszwalb, P., Huttenlocher, D.: Pictorial structures for object recognition. IJCV 61(1), 55–79 (2005)CrossRef
13.
Oktay, A.B., Akgul, Y.S.: Simultaneous localization of lumbar vertebrae and intervertebral discs with SVM based MRF. IEEE Trans. Med. Imaging 1179–1182 (2013)
14.
Ghosh, S., Malgireddy, M.R., Chaudhary, V., Dhillon, G.: A new approach to automatic disc localization in clinical lumbar MRI: Combining machine learning with heuristics. In: International Symposium on Biomedical Imaging (2012)
15.
Zhan, Y., Maneesh, D., Harder, M., Zhou, X.S.: Robust MR spine detection using hierarchical learning and local articulated model. Med. Image Comput. Comput.-Assist. Interv.—MICCAI—LNCS 7510, 141–148 (2012)
16.
Chwialkowski, M.P., Shile, P.E., Pfeifer, D., Parkey, R.W., Peshock, R.M.: Automated localization and identification of lower spinal anatomy in magnetic resonance images. Comput. Biomed. Res. 24(2) (1989)
17.
Aslan, M.S., Ali, A., Rara, H., Farag, A.A.: An automated vertebra identification and segmentation in CT images. In: Proceedings of IEEE 17th International Conference on Image Processing (2010)
18.
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: Medical Image Computing and Computer-Assisted Intervention (2012)
19.
Pekar, V., Bystrov, D., Heese, H.S., Dries, S.P.M., Schmidt, S., Grewer, R., Harder, C.J.D., Bergmans, R.C., Simonetti, A.W., Muiswinkel, A.M.V.: Automated planning of scan geometries in spine mri scans. In: Medical Image Computing and Computer-Assisted Intervention, vol. 10, pp. 601–608 (2007)
20.
Alomari, R.S., Corso, J.J., Chaudhary, V.: 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 (2011)CrossRef
21.
Kelm, B.M., Wels, M., Zhou, K.S., Seifert, S., Suehling, M., Zheng, Y., Comaniciu, D.: Spine detection in ct and mr using iterated marginal space learning. Med. Image Anal (2012)
22.
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. 13(3), 471–482 (2009)CrossRef
23.
Felzenszwalb, P.F., Grishick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE PAMI 32(9), 1627–1645 (2010)CrossRef
24.
Potesil, V., Lootus, M., El-Labban, A., Kadir, T.: Landmark localization in images with variable field of view. In: International Symposium on Biomedical Imaging (2013)
Metadaten
Titel
Vertebrae Detection and Labelling in Lumbar MR Images
verfasst von
Meelis Lootus
Timor Kadir
Andrew Zisserman
Copyright-Jahr
2014
Buch
Computational Methods and Clinical Applications for Spine Imaging

Print ISBN: 978-3-319-07268-5

Electronic ISBN: 978-3-319-07269-2

Copyright-Jahr: 2014

DOI
https://doi.org/10.1007/978-3-319-07269-2_19