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Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance

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Abstract

Purpose

More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations.

Methods

The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise; (2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors; and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated.

Results

The material is composed of a set of 90 CT chest volumes. We obtained high accuracy parameters, such as Overlap Dice (%) = 98.77 ± 0.58 and False Negative (%) = 0.13 ± 0.026.

Conclusion

The proposed methodology presented significant accuracy values and processing time, having a large database to test the methodology, besides being a fully automatic method of 3D bone reconstruction.

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References

  • Allisy-Roberts P, Williams J. Farr’s physics for medical imaging. 2nd ed. USA: Elsevier; 2008.

    Google Scholar 

  • Aminoff MJ, Boller F, Swaab DF. Handbook of clinical neurology. USA: Elsevier; 2002.

    Google Scholar 

  • Armato SG 3rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys. 2011;38(2):915–31.

    Article  Google Scholar 

  • Auer R, Riehl J. The incidence of deep vein thrombosis and pulmonary embolism after fracture of the tibia: An analysis of the National Trauma Databank. J Clin Orthop Trauma. 2017;8(1):38–44.

  • Bondy A, Murty USR. Graph theory. 1st ed. London: Springer; 2007.

    MATH  Google Scholar 

  • Calder J, Tahmasebi AM. A variational approach to bone segmentation in CT images. Proc SPIE Int Soc Opt Eng. 2011;7962(3):346–54.

    Google Scholar 

  • Cormem TH, Leiserson CE, Rivest RL, Stein C. Introduction to algorithms. 3rd ed. England: The MIT Press; 2009.

    Google Scholar 

  • Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297–302.

    Article  Google Scholar 

  • Drumond DAF, Abranates WL. Tipos de trauma – o politraumatizado. In: Freire E. (Org.). Trauma: a doença dos séculos. São Paulo: Atheneu; 2001. p. 451–59.

  • Ferraz Jr. SUS gasta R$ 4 milhões para atender acidentados em um ano. Jornal da USP. 2018. https://jornal.usp.br/atualidades/sus-gasta-r-4-milhoes-para-atender-acidentados-em-um-ano/. Accessed in 09 Jan 2019.

  • Florence L, Córdova LA, Pajarinen J, Lin T, Yao Z, Goodman SB. Inflammation, fracture and bone repair. Bone. 2016;86:119–30.

    Article  Google Scholar 

  • Freitas MB. Panorama das Exposições Médicas em Radiologia Convencional no Estado de São Paulo. PhD [thesis]. São Paulo: Universidade Federal de São Paulo. 2005.

  • Gardner MW, Dorling SR. Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmos Environ. 1998;32(14–15):2627–36.

    Article  Google Scholar 

  • González BA, Mahesh M, Kim KP, Bhargavan M, Lewis R, Mettler F, et al. Projected cancer risks from computed tomographic scans performed in the United States in 2007. Arch Intern Med. 2007;169(22):2071–7.

    Article  Google Scholar 

  • Haykin S. Neural networks and learning machines. 3rd ed. Hamilton: Pearson Prentice Hall; 2009.

    Google Scholar 

  • Huang L, Qiu Z, Song Z. 3D reconstruction and visualization from 2D CT images. IEEE. 2011;2(1):153–7.

    Google Scholar 

  • Kamencay P, Zachariasova M, Hudec R, Benco M, Radil R. 3D image reconstruction from 2D CT slices. IEEE. 2014;1(1):1–4.

    MathSciNet  Google Scholar 

  • Kareliotis G. Study of kVp and mAs effect on radiation dose and image quality in computed tomography. Master [thesis]. Greece: National Technical University of Athens; 2015.

  • Kupinsk MA, Giger ML. Automated seeded lesion segmentation on digital mammograms. IEEE Trans Med Imaging. 1998;17(4):510–7.

    Article  Google Scholar 

  • Marder VJ, Aird WC, Bennett JS, Chulman S, White GC II. Hemostasis and thrombosis: basic principles and clinical practice. 6th ed. USA: Lippincott William & Wilkins; 2012.

    Google Scholar 

  • Nakajima Y, Yamada K, Imamura K. Radiologist supply and workload: international comparison--working Group of Japanese College of Radiology. Radiat Med. 2008;26(8):455–65.

    Article  Google Scholar 

  • Rezende SO. Sistemas Inteligentes: Fundamentos e Aplicações. Barueri: Manole; 2003.

    Google Scholar 

  • Russo P. Handbook of X-ray imaging: physics and technology. 1st ed. London: CRC Press; 2018.

    Google Scholar 

  • Schmid GL, Lippmann S, Unverzagt S, Hofmann C, Deutsch T, Frese T. The investigation of suspected fracture - a comparison of ultrasound with conventional imaging. Dtsch Arztebl Int. 2017;114(45):757–64.

    Google Scholar 

  • Shadid WG, Willis A. Bone fragment segmentation from 3D CT imagery. Comput Med Imaging Graph. 2018;66(1):14–27.

    Article  Google Scholar 

  • Singh G, Neeraj S. 3-D visualization techniques for medical images: a comprehensive study. IJIRCCE. 2016;4(7):14321–4.

    Google Scholar 

  • Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006;30(2):75–87.

    Article  Google Scholar 

  • Wirth N. Algorithms + data structures = programs. USA: Prentice Hall PTR; 1978.

    MATH  Google Scholar 

  • Zhao B, Ding H, Lu Y, Wang G, Zhao J, Molloi S. Dual-dictionary learning-based iterative image reconstruction for spectral computed tomography application. Phys Med Biol. 2012;57(24):8217–29.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the Coordination for the Improvement of Higher Education Personnel (CAPES), Laboratory of Image and Signal Processing of the Institute of Science and Technology of UNIFESP (LaPIS-ICT-UNIFESP), the Trauma Team of the Municipal Hospital of São José dos Campus under the coordination of Physician Luciano Guedes Jorge, and The unknown reviewers, who have made important contributions to this work.

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Authors and Affiliations

  1. Laboratory of Image and Signal Processing of the Institute of Science and Technology, Federal University of São Paulo – UNIFESP, 330 Talim St. room 108, Jardim Aeroporto, CEP, São José dos Campos, SP, 12231-280, Brazil

    Barbara Teixeira Sais, Daniel Aparecido Vital & Matheus Cardoso Moraes

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  1. Barbara Teixeira Sais
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  2. Daniel Aparecido Vital
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  3. Matheus Cardoso Moraes
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Correspondence to Barbara Teixeira Sais.

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Sais, B.T., Vital, D.A. & Moraes, M.C. Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance. Res. Biomed. Eng. 35, 45–56 (2019). https://doi.org/10.1007/s42600-019-00008-z

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  • DOI: https://doi.org/10.1007/s42600-019-00008-z

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