Top

Published in:

2019 | OriginalPaper | Chapter

Intervertebral Disc Segmentation and Localization from Multi-modality MR Images with 2.5D Multi-scale Fully Convolutional Network and Geometric Constraint Post-processing

Authors : Chang Liu, Liang Zhao

Published in: Computational Methods and Clinical Applications for Spine Imaging

Publisher: Springer International Publishing

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The intervertebral discs (IVDs) segmentation and localization on medical images are important for the clinical diagnosis and research of spine diseases. In this work, we proposed a robust automatic method based on 2.5D multi-scale fully convolutional network (FCN) and geometric constraint post-processing for IVD segmentation and localization on 3D multi-modality Magnetic Resonance (MR) scans. Firstly, we designed a 2.5D multi-scale FCN. And the ensemble outputs of such three networks are used as the IVD prediction maps. The final segmentation and localization of IVDs are generated from these prediction maps with a geometric constraint post-processing method. This work ranked the first in the on-site test of MICCAI 2018 Challenge on Automatic Intervertebral Disc Localization and Segmentation from 3D Multi-modality MR Images (IVDM3Seg).

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter IVD-Net: Intervertebral Disc Localization and Segmentation in MRI with a Multi-modal UNet

next chapter Automatic Segmentation of Lumbar Spine MRI Using Ensemble of 2D Algorithms

An, H.S., et al.: Introduction: disc degeneration: summary. Spine 29, 2677–2678 (2004)CrossRef

Urban, J.P., Roberts, S.J.A.R.T.: Degeneration of the intervertebral disc. Arthritis Res. Ther. 5, 120 (2003)CrossRef

Maniadakis, N., Gray, A.J.P.: The economic burden of back pain in the UK. Pain 84, 95–103 (2000)CrossRef

Luoma, K., Riihimäki, H., Luukkonen, R., Raininko, R., Viikari-Juntura, E., Lamminen, A.J.S.: Low back pain in relation to lumbar disc degeneration. Spine 25, 487–492 (2000)CrossRef

Niemeläinen, R., Videman, T., Dhillon, S., Battié, M.: Quantitative measurement of intervertebral disc signal using MRI. Clin. Radiol. 63, 252–255 (2008)CrossRef

Violas, P., Estivalezes, E., Briot, J., de Gauzy, J.S., Swider, P.: Objective quantification of intervertebral disc volume properties using MRI in idiopathic scoliosis surgery. Magn. Reson. Imaging 25, 386–391 (2007)CrossRef

Neubert, A., et al.: Automated 3D segmentation of vertebral bodies and intervertebral discs from MRI. In: 2011 International Conference on Digital Image Computing Techniques and Applications (DICTA), pp. 19–24 (2011)

Corso, J.J., Alomari, R.S., Chaudhary, V.: Lumbar disc localization and labeling with a probabilistic model on both pixel and object features. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 202–210. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_25CrossRef

Chevrefils, C., Chériet, F., Grimard, G., Aubin, C.-E.: Watershed segmentation of intervertebral disk and spinal canal from MRI images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2007. LNCS, vol. 4633, pp. 1017–1027. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74260-9_90CrossRef

10.

Ben Ayed, I., Punithakumar, K., Garvin, G., Romano, W., Li, S.: Graph cuts with invariant object-interaction priors: application to intervertebral disc segmentation. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 221–232. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22092-0_19CrossRef

11.

Raja’S, A., 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, 1–10 (2011)CrossRef

12.

Chevrefils, C., Cheriet, F., Aubin, C.É., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from MR images. IEEE Trans. Inf Technol. Biomed. 13, 608–620 (2009)CrossRef

13.

Schmidt, S., et al.: Spine detection and labeling using a parts-based graphical model. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 122–133. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-73273-0_11CrossRef

14.

Kelm, B.M., et al.: Spine detection in CT and MR using iterated marginal space learning. Med. Image Anal. 17, 1283–1292 (2013)CrossRef

15.

Huang, S.-H., Chu, Y.-H., Lai, S.-H., Novak, C.L.: Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI. IEEE Trans. Med. Imaging 28, 1595–1605 (2009)CrossRef

16.

Wang, Z., Zhen, X., Tay, K., Osman, S., Romano, W., Li, S.: Regression segmentation for M³ spinal images. IEEE Trans. Med. Imaging 34, 1640–1648 (2015)CrossRef

17.

Li, X., et al.: 3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images. Med. Image Anal. 45, 41–54 (2018)CrossRef

18.

Chen, H., Dou, Q., Wang, X., Qin, J., Cheng, J.C.Y., Heng, P.-A.: 3D fully convolutional networks for intervertebral disc localization and segmentation. In: Zheng, G., Liao, H., Jannin, P., Cattin, P., Lee, S.-L. (eds.) MIAR 2016. LNCS, vol. 9805, pp. 375–382. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43775-0_34CrossRef

19.

Zhang, W., et al.: Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage 108, 214–224 (2015)CrossRef

20.

Havaei, M., Guizard, N., Chapados, N., Bengio, Y.: HeMIS: hetero-modal image segmentation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 469–477. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_54CrossRef

21.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

22.

Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507, vol. 7 (2017)

23.

Kjaer, P., Leboeuf-Yde, C., Korsholm, L., Sorensen, J.S., Bendix, T.: Magnetic resonance imaging and low back pain in adults: a diagnostic imaging study of 40-year-old men and women. Spine 30, 1173–1180 (2005)CrossRef

Title: Intervertebral Disc Segmentation and Localization from Multi-modality MR Images with 2.5D Multi-scale Fully Convolutional Network and Geometric Constraint Post-processing
Authors: Chang Liu
Liang Zhao
Publisher: Springer International Publishing
Book: Computational Methods and Clinical Applications for Spine Imaging
Print ISBN: 978-3-030-13735-9

Electronic ISBN: 978-3-030-13736-6

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-13736-6_12

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner