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Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

A fully automated segmentation algorithm, progressive surface resolution (PSR), is presented in this paper to determine the closed surface of approximately convex blob-like structures that are common in biomedical imaging. The PSR algorithm was applied to the cortical surface segmentation of 460 vertebral bodies on 46 low-dose chest CT images, which can be potentially used for automated bone mineral density measurement and compression fracture detection.

Methods

The target surface is realized by a closed triangular mesh, which thereby guarantees the enclosure. The surface vertices of the triangular mesh representation are constrained along radial trajectories that are uniformly distributed in 3D angle space. The segmentation is accomplished by determining for each radial trajectory the location of its intersection with the target surface. The surface is first initialized based on an input high confidence boundary image and then resolved progressively based on a dynamic attraction map in an order of decreasing degree of evidence regarding the target surface location.

Results

For the visual evaluation, the algorithm achieved acceptable segmentation for 99.35 % vertebral bodies. Quantitative evaluation was performed on 46 vertebral bodies and achieved overall mean Dice coefficient of 0.939 (with max \(=\) 0.957, min \(=\) 0.906 and standard deviation \(=\) 0.011) using manual annotations as the ground truth.

Conclusions

Both visual and quantitative evaluations demonstrate encouraging performance of the PSR algorithm. This novel surface resolution strategy provides uniform angular resolution for the segmented surface with computation complexity and runtime that are linearly constrained by the total number of vertices of the triangular mesh representation.

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References

  1. McInerney T, Terzopoulos D (1995) A dynamic finite element surface model for segmentation and tracking in multidimensional medical images with application to cardiac 4D image analysis. Comput Med Imaging Graph 19(1):69–83

    Article  CAS  PubMed  Google Scholar 

  2. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  3. Mastmeyer A, Engelke K, Fuchs C, Kalender WA (2006) A hierarchical 3D segmentation method and the definition of vertebral body coordinate systems for QCT of the lumbar spine. Med Image Anal 10(4):560–577

    Article  PubMed  Google Scholar 

  4. Cohen LD, Cohen I (1993) Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans Pattern Anal Mach Intell 15(11):1131–1147

    Article  Google Scholar 

  5. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  CAS  PubMed  Google Scholar 

  6. 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 

  7. Malladi R, Sethian J, Vemuri BC (1995) Shape modeling with front propagation: a level set approach. IEEE Trans Pattern Anal Mach Intell 17(2):158–175

    Article  Google Scholar 

  8. Klinder T, Ostermann J, Ehm M, Franz A, Kneser R, Lorenz C (2009) Automated model-based vertebra detection, identification, and segmentation in CT images. Medical image analysis 13(3):471–482

    Article  PubMed  Google Scholar 

  9. Reed VK, Woodward WA, Zhang L, Strom EA, Perkins GH, Tereffe W, Oh JL, Yu TK, Bedrosian I, Whitman GJ, Buchholz TA, Dong L (2009) Automatic segmentation of whole breast using atlas approach and deformable image registration. Int J Radiat Oncol Biol Phys 73(5):1493–1500

    Article  PubMed  PubMed Central  Google Scholar 

  10. Al-Helo S, Alomari RS, Ghosh S, Chaudhary V, Dhillon G, Moh’d BA, Hiary H, Hamtini TM (2013) Compression fracture diagnosis in lumbar: a clinical CAD system. Int J Comput Assist Radiol Surg 8(3):461–469

    Article  PubMed  Google Scholar 

  11. Leventon ME, Grimson WEL, Faugeras O (2000) Statistical shape influence in geodesic active contours. In: CVPR, pp 316–323

  12. Smyth PP, Taylor CJ, Adams JE (1997) Automatic measurement of vertebral shape using active shape models. In: IPMI, pp 441–446

  13. Reeves AP, Russell WT (1989) Identification of three-dimensional objects using range information. IEEE Trans Pattern Anal Mach Intell 11(4):403–410

    Article  Google Scholar 

  14. Ritzel H, Amling M, Pösl M, Hahn M, Delling G (1997) The thickness of human vertebral cortical bone and its changes in aging and osteoporosis: a histomorphometric analysis of the complete spinal column from thirty-seven autopsy specimens. J Bone Miner Res 12(1):89–95

    Article  CAS  PubMed  Google Scholar 

  15. Shen H, Litvin A, Alvino C (2008) Localized priors for the precise segmentation of individual vertebras from ct volume data. In: MICCAI, pp 367–375

  16. Tan S, Yao J, Ward MM, Yao L, Summers RM (2007) 3D Multi-scale level set segmentation of vertebrae. In: ISBI, pp 896–899

  17. Štern D, Likar B, Pernuš F, Vrtovec T (2011) Parametric modelling and segmentation of vertebral bodies in 3D CT and MR spine images. Phys Med Biol 56(23):7505–7522

  18. Yao J, O’Connor SD, Summers RM (2006) Automated spinal column extraction and partitioning. In: ISBI, pp 390–393

  19. Benjelloun M, Mahmoudi S, Lecron F (2011) A framework of vertebra segmentation using the active shape model-based approach. J Biomed Imaging 9:1–14

    Article  Google Scholar 

  20. Naegel B (2007) Using mathematical morphology for the anatomical labeling of vertebrae from 3D CT-scan images. Comput Med Imaging Graph 31(3):141–156

    Article  PubMed  Google Scholar 

  21. Aslan MS, Ali A, Farag A, Abdelmumin H, Arnold B, Xiang P (2011) A new segmentation and registration approach for vertebral body analysis. In: ISBI, pp 2006–2009

  22. Lee J (2011) Automated analysis of anatomical structures from low-dose chest computed tomography scans. Dissertation, Cornell University

  23. Reeves AP, Biancardi AM, Yankelevitz DF, Fotin S, Keller BM, Jirapatnakul A, Lee J (2009) A public image database to support research in computer aided diagnosis. In: EMBC, pp 3715–3718

  24. Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79

    Article  Google Scholar 

  25. Johnson H, McCormick M, Ibanez L, and the Insight Software Consortium (2015) The ITK software guide fourth edition updated for ITK version 4.8, Kitware Inc, pp 385–388

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Acknowledgments

This work was funded in part by the Flight Attendants’ Medical Research Foundation (FAMRI).

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Shuang Liu, Yiting Xie & Anthony P. Reeves

Authors
  1. Shuang Liu
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  2. Yiting Xie
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  3. Anthony P. Reeves
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Corresponding author

Correspondence to Shuang Liu.

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Conflict of interest

Yiting Xie and Shuang Liu declare that they have no conflict of interest. Anthony Reeves financial and research disclosures: Financial (1) General Electric: Dr. Reeves is a co-inventor on a patent and other pending patents owned by Cornell Research Foundation (CRF) which are non-exclusively licensed and related to technology involving computer-aided diagnostic methods. (2) D4Vision Inc.: Dr. Reeves is the owner of D4Vision Inc. a company that licenses software for image analysis. Research Dr. Reeves receives research support in the form of grants and contracts from: NSF and the Flight Attendants’ Medical Research Institute.

Ethical standard

All the image data were de-identified, and from public databases; therefore, approval by an ethics committee was not applicable.

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Liu, S., Xie, Y. & Reeves, A.P. Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images. Int J CARS 11, 789–801 (2016). https://doi.org/10.1007/s11548-015-1320-0

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  • DOI: https://doi.org/10.1007/s11548-015-1320-0

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