Localization and Labeling of Posterior Ribs in Chest Radiographs Using a CRF-regularized FCN with Local Refinement

  • Alexander Oliver MaderEmail author
  • Jens von Berg
  • Alexander Fabritz
  • Cristian Lorenz
  • Carsten Meyer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Localization and labeling of posterior ribs in radiographs is an important task and a prerequisite for, e.g., quality assessment, image registration, and automated diagnosis. In this paper, we propose an automatic, general approach for localizing spatially correlated landmarks using a fully convolutional network (FCN) regularized by a conditional random field (CRF) and apply it to rib localization. A reduced CRF state space in form of localization hypotheses (generated by the FCN) is used to make CRF inference feasible, potentially missing correct locations. Thus, we propose a second CRF inference step searching for additional locations. To this end, we introduce a novel “refine” label in the first inference step. For “refine”-labeled nodes, small subgraphs are extracted and a second inference is performed on all image pixels. The approach is thoroughly evaluated on 642 images of the public Indiana chest X-ray collection, achieving a landmark localization rate of 94.6%.


Posterior ribs Localization and labeling Chest radiography Fullyconvolutional network Conditional random field 



This work has been financially supported by the Federal Ministry of Education and Research under the grant 03FH013IX5. The liability for the content of this work lies with the authors.

Supplementary material

473975_1_En_63_MOESM1_ESM.pdf (2 mb)
Supplementary material 1 (pdf 2060 KB)


  1. 1.
    Candemir, S., et al.: Atlas-based rib-bone detection in chest X-rays. CMIG 51, 32–39 (2016)Google Scholar
  2. 2.
    von Berg, J., et al.: A novel bone suppression method that improves lung nodule detection. IJCARS 11(4), 641–655 (2016)Google Scholar
  3. 3.
    Loog, M., Ginneken, B.: Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification. T-MI 25(5), 602–611 (2006)Google Scholar
  4. 4.
    von Berg, J., et al.: Decomposing the bony thorax in X-ray images. In: ISBI, pp. 1068–1071 (2016)Google Scholar
  5. 5.
    Staal, J., Ginneken, B., Viergever, M.: Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data. MIA 11(1), 35–46 (2007)Google Scholar
  6. 6.
    Wu, D., et al.: A learning based deformable template matching method for automatic rib centerline extraction and labeling in CT images. In: CVPR, pp. 980–987. IEEE (2012)Google Scholar
  7. 7.
    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). Scholar
  8. 8.
    U.S. National Library of Medicine (NLM): Open-i Open Access Biomedical Image Search Engine (2017). Accessed 14 Feb 2018
  9. 9.
    Bergtholdt, M., Kappes, J.H., Schnörr, C.: Learning of graphical models and efficient inference for object class recognition. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 273–283. Springer, Heidelberg (2006). Scholar
  10. 10.
    Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2014)Google Scholar
  11. 11.
    Mader, A.O., et al.: Detection and localization of landmarks in the lower extremities using an automatically learned conditional random field. In: GRAIL (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alexander Oliver Mader
    • 1
    • 2
    • 3
    Email author
  • Jens von Berg
    • 3
  • Alexander Fabritz
    • 2
  • Cristian Lorenz
    • 3
  • Carsten Meyer
    • 1
    • 2
    • 3
  1. 1.Institute of Computer ScienceKiel University of Applied SciencesKielGermany
  2. 2.Department of Computer Science, Faculty of EngineeringKiel UniversityKielGermany
  3. 3.Department of Digital ImagingPhilips Research HamburgHamburgGermany

Personalised recommendations