Skip to main content
SpringerLink
Log in

Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization

  • Chapter
  • First Online:
Computational Intelligence in Biomedical Imaging

Abstract

Automatic segmentation of lung tissue in thoracic CT scans is useful for diagnosis and treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to its higher attenuation, inhomogeneous appearance, and inconsistent texture. We overcome these challenges through a multi-layer machine learning approach that exploits geometric structures both within and outside the lung (e.g., ribs, spine). In the coarsest layer, a set of stable landmarks on the surface of the lung are detected through a hierarchical detection network (HDN) that is trained on hundreds of annotated CT volumes. These landmarks are used to robustly initialize a coarse statistical model of the lung shape. Subsequently, a region-dependent boundary refinement uses a discriminative appearance classifier to refine the surface, and finally a region-driven level set refinement is used to extract the fine-scale detail. Through this approach we demonstrate robustness to a variety of lung pathologies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The initial analysis and comparison of the performance of the boundary landmarks also appears in our earlier conference publication [30].

References

  1. Abe Y, Hanai K, Nakano M, Ohkubo Y, Hasizume T, Kakizaki T, Nakamura M, Niki N, Eguchi K, Fujino T, Moriyama N (2005) A computer-aided diagnosis (CAD) system in lung cancer screening with computed tomography. Anticancer Res 25:483–488

    Google Scholar 

  2. Adams H, Bernard M, McConnochie K (1991) An appraisal of CT pulmonary density mapping in normal subjects. Clin Radiol 43(4):238–42

    Article  Google Scholar 

  3. Armato III S, Sensakovic W (2004) Automated lung segmentation for thoracic CT: Impact on computer-aided diagnosis. Acad Radiol 11(9):1011–1021

    Article  Google Scholar 

  4. Brown MS, McNitt-Gray MF, Mankovich NJ, Goldin JG, Hiller J, Wilson LS, Aberle DR (1997) Method for segmenting chest CT image data using an anatomical model: preliminary results. IEEE Trans Med Imag 16(6):828–839

    Article  Google Scholar 

  5. Chan T, Vese L (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Article  MATH  Google Scholar 

  6. Cootes T, Hill A, Taylor C, Haslam J (1994) Use of active shape models for locating structures in medical images. Image Vis Comput 12(6):355–365

    Article  Google Scholar 

  7. Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: Integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215

    Article  Google Scholar 

  8. Haider C, Bartholmai B, Holmes D, Camp J, Robb R (2005) Quantitative characterization of lung disease. Comput Med Imag Graph 29(7):555–563

    Article  Google Scholar 

  9. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621

    Article  Google Scholar 

  10. Hedlund L, Anderson R, Goulding P, Beck J, Effmann E, Putman C (1982) Two methods for isolating the lung area of a CT scan for density information. Radiology 144(2):353–357

    Google Scholar 

  11. Hu S, Hoffman E, Reinhardt J (2002) Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imag 20(6):490–498

    Article  Google Scholar 

  12. Hua P, Song Q, Sonka M, Hoffman E, Reinhardt J (2011) Segmentation of pathological and diseased lung tissue in CT images using a graph-search algorithm. International Symposium on Biomedical Imaging, Chicago, IL, March 2011

    Google Scholar 

  13. International Consensus Statement (2000) Idiopathic pulmonary fibrosis: diagnosis and treatment. American Thoracic Society (ATS) and the European Respiratory Society (ERS), vol 161, pp 646–664

    Google Scholar 

  14. International Consensus Statement (2002) American thoracic society/European respiratory society international multidisciplinary consensus classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med 165:277–304

    Article  Google Scholar 

  15. Kanazawa K, Kawata Y, Niki N, Satoh H, Ohmatsu H, Kakinuma R, Kaneko M, Moriyama N, Eguchi K (1998) Computer-aided diagnosis for pulmonary nodules based on helical CT images. Comput Med Imag Graph 22(2):157–167

    Article  Google Scholar 

  16. King TE (2005) Clinical advances in the diagnosis and therapy of the interstitial lung diseases. Am J Respir Crit Care Med 172(3):268–279 (2005)

    Google Scholar 

  17. Kockelkorn T, van Rikxoort E, Grutters J, van Ginneken B (2010) Interactive lung segmentation in CT scans with severe abnormalities. In: International Symposium on Biomedical Imaging, Rotterdam, Netherlands, pp 564–567, 14–17 April 2010

    Google Scholar 

  18. Kohlberger T, Sofka M, Zhang J, Birkbeck N, Wetzl J, Kaftan J, Declerck J, Zhou S (2011) Automatic multi-organ segmentation using learning-based segmentation and level set optimization. In: Mediacl Image Computing and Computer-assisted Iintervention 2011

    Google Scholar 

  19. Kohlberger T, Uzunbaş MG, Alvino C, Kadir T, Slosman DO, Funka-Lea G (2009) Organ segmentation with level sets using local shape and appearance priors. In: Proceedings of MICCAI: Part II. pp 34–42. MICCAI ’09

    Google Scholar 

  20. Korfiatis P, Kalogeropoulou C, Karahaliou A, Kazantzi A, Skiadopoulos S, Costaridou L (2008) Texture classification-based segmentation of lung affected by interstitial pneumonia in high-resolution CT. Med phys 35:5290

    Google Scholar 

  21. Ling H, Zhou SK, Zheng Y, Georgescu B, Suehling M, Comaniciu D (2008) Hierarchical, learning-based automatic liver segmentation. In: CVPR, pp 1–8, IEEE Computer Society, Los Alamitos, CA, USA, 2008

    Google Scholar 

  22. Prasad M, Brown M, Ahmad S, Abtin F, Allen J, da Costa I, Kim H, McNitt-Gray M, Goldin J (2008) Automatic segmentation of lung parenchyma in the presence of diseases based on curvature of ribs. Acad. Radiol. 15(9):1173–1180

    Article  Google Scholar 

  23. Pu J, Roos J, Yi C, Napel S, Rubin G, Paik D (2008) Adaptive border marching algorithm: Automatic lung segmentation on chest CT images. Comput Med Imag Graph 32(6):452–462

    Article  Google Scholar 

  24. Reeves AP, Kostis WJ (2000) Computer-aided diagnosis for lung cancer. Radiol Clin North Am. 38(3):497–509

    Article  Google Scholar 

  25. Robinson P, Krell L (1979) Pulmonary tissue attenuation with computed tomography: comparison of inspiration and expiration scans. J Comput Assist Tomogr 3(6):740–748

    Google Scholar 

  26. Silva A, Silva JS, Santos BS, Ferreira C (2001) Fast pulmonary contour extraction in X-ray CT images: A methodology and quality assessment. In: SPIE Conf on Medical Imaging: Physiology and Function from Multidimensional Images. vol 4321, pp 216–224

    Google Scholar 

  27. Sluimer I, Schilham A, Prokop M, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imag 25(4):385–405

    Article  Google Scholar 

  28. Sluimer I, Prokop M, van Ginneken B (2005) Toward automated segmentation of the pathological lung in CT. IEEE Trans Med Imag 24(8):1025–1038

    Article  Google Scholar 

  29. Sofka M, Ralovich K, Birkbeck N, Zhang J, Zhou S (2011) Integrated detection network (IDN) for pose and boundary estimation in medical images. In: Proceedings of the 8th International Symposium on Biomedical Imaging (ISBI 2011). Chicago, IL, 30 March–2 April 2011

    Google Scholar 

  30. Sofka M, Wetzl J, Birkbeck N, Zhang J, Kohlberger T, Kaftan J, Declerck J, Zhou S (2011) Multi-stage learning for robust lung segmentation in challenging CT volumes. In: Medical Image Computing and Computer-Assisted Intervention, MICCAI ’11, Toronto, Canada, 18–22 September 2011

    Google Scholar 

  31. Sofka M, Zhang J, Zhou S, Comaniciu D: Multiple object detection by sequential Monte Carlo and hierarchical detection network. In: IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, 13–18 June 2010

    Google Scholar 

  32. Sun S, McLennan G, Hoffman EA, Beichel R (2010) Model-based segmentation of pathological lungs in volumetric ct data. In: The Third International Workshop on Pulmonary Image Analysis, Beijing, 20 September 2010

    Google Scholar 

  33. Tu Z (2005) Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: International Conference on Computer Vision, ICCV 2005, pp 1589–1596, vol 2,Beijing, 17–21 October 2005

    Google Scholar 

  34. Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang CT, Suzuki K, Doi K (2003) Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography. Med Phys 30:2440–2454

    Article  Google Scholar 

  35. Ukil S, Reinhardt J (2009) Anatomy-guided lung lobe segmentation in X-ray CT images. IEEE Trans Med Image 28(2):202–214

    Article  Google Scholar 

  36. Ukil S, Reinhardt JM (2005) Smoothing lung segmentation surfaces in three-dimensional X-ray CT images using anatomic guidance. Acad. Radiol 12(12):1502–1511

    Article  Google Scholar 

  37. Uppaluri R, Hoffman EA, Sonka M, Hartley PG, Hunninghake GW, McLennan G (1999) Computer recognition of regional lung disease patterns. Am J Respir Crit Care Med 160(2):648–654

    Article  Google Scholar 

  38. Uppaluri R, Mitsa T, Sonka M, Hoffman EA, Mclennan G (1997) Quantification of pulmonary emphysema from lung computed tomography images. Am J Respir Crit Care Med 156:248–254

    Article  Google Scholar 

  39. Wang J, Li F, Li Q (2009) Automated segmentation of lungs with severe interstitial lung disease in CT. Med phys 36:4592

    Google Scholar 

  40. Wang J, Lib F, Doib K, Lia Q (2009) A novel scheme for detection of diffuse lung disease in MDCT by use of statistical texture features. In: SPIE 7260, 27 February 2009

    Google Scholar 

  41. Zhang L, Hoffman E, Reinhardt J (2005) Atlas-driven lung lobe segmentation in volumetric X-ray CT images. IEEE Trans Med Image 25(1):1–16

    Article  MATH  Google Scholar 

  42. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2007) Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. In: IEEE International Conference on Computer Vision, ICCV 2007, pp 1–8, Rio de Janeiro, 14–21 October 2007

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Siemens Corp., Corporate Technology, 755 College Road East, Plainsboro, NJ, 08540, USA

    Neil Birkbeck, Michal Sofka, Timo Kohlberger, Jingdan Zhang, Jens Wetzl & S. Kevin Zhou

  2. Siemens Molecular Imaging, 23–38 Hythe Bridge Street, Oxford, OX1 2EP, UK

    Jens Kaftan

Authors
  1. Neil Birkbeck
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michal Sofka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Timo Kohlberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingdan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jens Wetzl
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jens Kaftan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. S. Kevin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Neil Birkbeck .

Editor information

Editors and Affiliations

  1. Department of Radiology, University of Chicago, Chicago, Illinois, USA

    Kenji Suzuki

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media New York

About this chapter

Cite this chapter

Birkbeck, N. et al. (2014). Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization. In: Suzuki, K. (eds) Computational Intelligence in Biomedical Imaging. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7245-2_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4614-7245-2_8

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-7244-5

  • Online ISBN: 978-1-4614-7245-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation