Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Localization of Bone Surfaces from Ultrasound Data Using Local Phase Information and Signal Transmission Maps Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images

verfasst von : Guodong Zeng, Guoyan Zheng

Erschienen in: Computational Methods and Clinical Applications in Musculoskeletal Imaging

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper addresses the challenging problem of segmentation of intervertebral discs (IVDs) in three-dimensional (3D) T2-weighted magnetic resonance (MR) images. We propose a deeply supervised multi-scale fully convolutional network for segmentation of IVDs in 3D MR images. After training, our network can directly map a whole volumetric data to its volume-wise labels. Multi-scale deep supervision is designed to alleviate the potential gradient vanishing problem during training. It is also used together with partial transfer learning to boost the training efficiency when only small set of labeled training data are available. The present method was validated on the MICCAI 2015 IVD segmentation challenge datasets. Our method achieved a mean Dice overlap coefficient of 92.0% and a mean average symmetric surface distance of 0.41 mm. The results achieved by our method are better than those achieved by the state-of-the-art methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Vorheriges Kapitel Classification of Osteoporotic Vertebral Fractures Using Shape and Appearance Modelling
Literatur
1.
Modic, M., Ross, J.: Lumbar degenerative disk disease. Radiology 245(1), 43–61 (2007)CrossRef
2.
3.
An, H., Anderson, P., Haughton, V., Iatridis, J., Kang, J., Lotz, J., Natarajan, R., Oegema, T.J., Roughley, P., Setton, L., Urban, J., Videman, T., Andersson, G., Weinstein, J.: Introduction: disc degeneration: summary. Spine 29(23), 2677–2678 (2004)CrossRef
4.
Chevrefils, C., Cheriet, F., Aubin, C., Grimard, G.: Texture analysis for automatic segmentation of intervertebral disks of scoliotic spines from MR images. IEEE Trans. Inf Technol. Biomed. 13(4), 608–620 (2009)CrossRef
5.
Michopoulou, S., Costaridou, L., Panagiotopoulos, E., Speller, R., Panayiotakis, G., Todd-Pokropek, A.: Atlas-based segmentation of degenerated lumbar intervertebral discs from MR images of the spine. IEEE Trans. Biomed. Eng. 56(9), 2225–2231 (2009)CrossRef
6.
7.
Neubert, A., Fripp, J., Engstrom, C., Schwarz, R., Lauer, L., Salvado, O., Crozier, S.: Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys. Med. Biol. 57(24), 8457–8376 (2012)CrossRef
8.
Law, M., Tay, K., Leung, A., Garvin, G., Li, S.: Intervertebral disc segmentation in MR images using anisotropic oriented flux. Med. Image Anal. 17(1), 43–61 (2013)CrossRef
9.
Zheng, G., Chu, C., Belavý, D., Ibragimov, B., Korez, R., Vrtovec, T., Hutt, H., Everson, R., Meakin, J., Andrade, I., Glocker, B., Chen, H., Dou, Q., Heng, P., Wang, C., Forsberg, D., Neubert, A., Fripp, J., Urschler, M., Stern, D., Wimmer, M., Novikov, A., Cheng, H., Armbrecht, G., Felsenberg, D., Li, S.: Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: a grand challenge. Med. Image Anal. 35, 327–344 (2017)CrossRef
10.
11.
Kelm, M., Wels, M., Zhou, S., Seifert, S., Suehling, M., Zheng, Y., Comaniciu, D.: Spine detection in CT and MR using iterated marginal space learning. Med. Image Anal. 17(8), 1283–1292 (2013)CrossRef
12.
Chen, C., Belavy, D., Yu, W., Chu, C., Armbrecht, G., Bansmann, M., Felsenberg, D., Zheng, G.: Localization and segmentation of 3D intervertebral discs in MR images by data driven estimation. IEEE Trans. Med. Imaging 34(8), 1719–1729 (2015)CrossRef
13.
Wang, Z., Zhen, X., Tay, K., Osman, S., Romano, W., Li, S.: Regression segmentation for M\(^3\) spinal images. IEEE Trans. Med. Imaging 34(8), 1640–1648 (2015)CrossRef
14.
Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2(1), 1–127 (2009)CrossRef
15.
Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., et al. (eds.) Proceedings of Neural Information Processing Systems – NIPS 2012, vol. 25, pp. 1097–1105. NIPS (2012)
16.
Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., Nielsen, M.: Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 246–253. Springer, Heidelberg (2013). https://​doi.​org/​10.​1007/​978-3-642-40763-5_​31CrossRef
17.
18.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2015, pp. 3431–3440 (2015)
19.
Roth, H., Yao, J., Lu, L., Stieger, J., Burns, J., Summers, R.: Detection of sclerotic spine metastases via random aggregation of deep convolutional neural network classifications. In: Yao, J., et al. (eds.) Proceedings of 2nd MICCAI Workshop on Computational Methods and Clinical Applications for Spine CSI 2014, LNCVB, vol. 20, pp. 3–12. Springer (2015). https://​doi.​org/​10.​1007/​978-3-319-14148-0_​1CrossRef
20.
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46723-8_​49CrossRef
21.
Milletari, F., Navab, N., Ahmadi, S.A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of 4th International Conference on 3D Vision - 3DV 2016, pp. 565–571. IEEE (2016)
22.
Dou, Q., Yu, L., Chen, H., Jin, Y., Yang, X., Qin, J., Heng, P.A.: 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017)CrossRef
23.
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
24.
25.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition - CVPR 2016, pp. 770–778. IEEE (2016)
26.
27.
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing. In: Proceedings of 32nd International Conference on Machine Learning - ICML 2015, vol. 37, pp. 448–456. PLMR (2015)
28.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., et al. (eds.) Proceedings of Advances in Neural Information Processing Systems - NIPS 2014, pp. 3320–3328. MIT Press (2014)
29.
Zeng, G., Yang, X., Li, J., Yu, L., Heng, P., Zheng, G.: 3D U-Net with multi-level deep supervision:fully automatic segmentation of proximal femur in 3D MR images. In: 8th MICCAI International Workshop on Machine Learning in Medical Imaging - MLMI 2017 (2017)
30.
Fang, Q., Boas, D.: Tetrahedral mesh generation from volumetric binary and gray-scale images. In: Proceedings of 6th IEEE International Symposium on Biomedical Imaging - ISBI 2009, pp. 1142–1145. IEEE (2009)
31.
Heimann, T., van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., Bello, F., Binnig, G., Bischof, H., Bornik, A., Cashman, P., Chi, Y., Cordova, A., Dawant, B., Fidrich, M., Furst, J., Furukawa, D., Grenacher, L., Hornegger, J., Kainmüller, D., Kitney, R., Kobatake, H., Lamecker, H., Lange, T., Lee, J., Lennon, B., Li, R., Li, S., Meinzer, H., Nemeth, G., Raicu, D., Rau, A., van Rikxoort, E., Rousson, M., Rusko, L., Saddi, K., Schmidt, G., Seghers, D., Shimizu, A., Slagmolen, P., Sorantin, E., Soza, G., Susomboon, R., Waite, J., Wimmer, A., Wolf, I.: Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans. Med. Imaging 28(8), 1251–1265 (2009)CrossRef
32.
Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching. J. ACM 45(6), 891–923 (1998)
33.
Karasawa, K., Oda, M., Kitasaka, T., Misawa, K., Fujiwara, M., Chu, C., Zheng, G., Rueckert, D., Mori, K.: Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med. Image Anal. 39, 18–28 (2017)CrossRef
34.
Korez, R., Ibragimov, B., Likar, B., Pernuš, F., Vrtovec, T.: Deformable model-based segmentation of intervertebral discs from MR spine images by using the SSC descriptor. In: Vrtovec, T., Yao, J., Glocker, B., Klinder, T., Frangi, A., Zheng, G., Li, S. (eds.) CSI 2015. LNCS, vol. 9402, pp. 117–124. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-41827-8_​11CrossRef
Metadaten
Titel
DSMS-FCN: A Deeply Supervised Multi-scale Fully Convolutional Network for Automatic Segmentation of Intervertebral Disc in 3D MR Images
verfasst von
Guodong Zeng
Guoyan Zheng
Copyright-Jahr
2018
Buch
Computational Methods and Clinical Applications in Musculoskeletal Imaging

Print ISBN: 978-3-319-74112-3

Electronic ISBN: 978-3-319-74113-0

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-74113-0_13

Premium Partner

    Bildnachweise