Tuberculosis Histopathology on X Ray CT

  • Ana Ortega-GilEmail author
  • Arrate Muñoz-Barrutia
  • Laura Fernandez-Terron
  • Juan José Vaquero
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)


Cutting-edge translational research on preclinical models of lung infectious diseases, such as Tuberculosis disease uses computed tomography (CT) images for assessing infection burden and drug efficacy over treatment. Biomarkers which characterize the distribution and extent of the disease-associated tissue are commonly based on the analysis of the intensity histogram as the involved tissues present abnormal densities in the organ being diagnosed. Often the cellular composition of the tissue represented by those grey-levels is ignored. Our hypothesis is that an accurate CT segmentation of the disease-associate tissue components could be based on the histopathological analysis of the sample. Drug development studies would then benefit of the efficacy assessment by lesion compartment response. We present here a protocol that allows to segment the healthy parenchyma, foamy macrophages and neutrophil foci in excised lung samples of healthy and tuberculous animal models.


Tuberculosis Micro-CT X-ray histology HU segmentation 



The research leading to these results received funding from the Innovative Medicines Initiative ( Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. This work was partially funded by projects RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK. This study (was supported by the Instituto de Salud Carlos III (Plan Estatal de I+D+i 2013–2016) and cofinanced by the European Social Fund (ESF) ‘‘ESF investing in your future’’. The authors would like to acknowledge Dr. Guembe from CIMA-Universidad de Navarra for preparing and staining the tissue sections and to Dr. Guerrero-Aspizua and Prof. Conti of the Department of Bioengineering, Universidad Carlos III de Madrid for the pathology evaluation.


  1. 1.
    Rayner, E.L., et al.: Early lesions following aerosol infection of rhesus macaques (macaca mulata) with mycobacterium tuberculosis strain H37RV. J. Comput. Pathol. 149(4), 475–485 (2013)CrossRefGoogle Scholar
  2. 2.
    Irwin, M.S., et al.: Presence of multiple lesion types with vastly different microenvironments in C3HeB/FeJ mice following aerosol infection with Mycobacterium tuberculosis. Dis. Model Mech. 8(6), 591–602 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Sharpe, S., et al.: Ultra low dose aerosol challenge with Mycobacterium tuberculosis leads to divergent outcomes in rhesus and cynomolgus macaques. Tuberculosis 96(Suppl. C), 1–12 (2016)CrossRefGoogle Scholar
  4. 4.
    Dartois, V.: The path of anti-tuberculosis drugs: from blood to lesions to mycobacterial cells. Nat. Rev. Microbiol. 12(3), 159–167 (2014)CrossRefGoogle Scholar
  5. 5.
    Pai, M., et al.: Tuberculosis. Nat. Rev. Dis. Prim. 2, 16076 (2016)CrossRefGoogle Scholar
  6. 6.
    Via, L.E., et al.: Infection dynamics and response to chemotherapy in a rabbit model of tuberculosis using [(1)(8)F]2-fluoro-deoxy-D-glucose positron emission tomography and computed tomography. Antimicrob. Agents Chemother. 56(8), 4391–4402 (2012)CrossRefGoogle Scholar
  7. 7.
    Galbán, C.J., et al.: Computed tomography–based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression. Nat. Med. 18(11), 1711–1715 (2012)CrossRefGoogle Scholar
  8. 8.
    Chen, R.Y., et al.: PET/CT imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis. Sci. Transl. Med. 6(265), 166 (2014)CrossRefGoogle Scholar
  9. 9.
    Via, L.E., et al.: A sterilizing tuberculosis treatment regimen is associated with faster clearance of bacteria in cavitary lesions in marmosets. Antimicrob. Agents Chemother. 59(7), 4181–4189 (2015)CrossRefGoogle Scholar
  10. 10.
    Volkman, H.E., Pozos, T.C., Zheng, J., Davis, J.M., Rawls, J.F., Ramakrishnan, L.: Tuberculous granuloma induction via interaction of a bacterial secreted protein with host epithelium. Science (80-) 327(5964), 466–469 (2010)CrossRefGoogle Scholar
  11. 11.
    Via, L.E., et al.: Differential virulence and disease progression following mycobacterium tuberculosis complex infection of the common marmoset (callithrix jacchus). Infect. Immun. 81(8), 2909–2919 (2013)CrossRefGoogle Scholar
  12. 12.
    Wallis, R.S., et al.: Tuberculosis biomarkers discovery: developments, needs, and challenges. Lancet Infect. Dis. 13(4), 362–372 (2013)CrossRefGoogle Scholar
  13. 13.
    Nachiappan, A.C., et al.: Pulmonary tuberculosis: role of radiology in diagnosis and management. RadioGraphics 37(1), 52–72 (2017)CrossRefGoogle Scholar
  14. 14.
    Lin, P.L., et al.: Radiologic responses in cynomolgous macaques for assessing tuberculosis chemotherapy regimens. Antimicrob. Agents Chemother. 57(9), 4237–4244 (2013)CrossRefGoogle Scholar
  15. 15.
    Mansoor, A., et al.: Segmentation and image analysis of abnormal lungs at CT: current approaches, challenges, and future trends. RadioGraphics 35(4), 1056–1076 (2015)CrossRefGoogle Scholar
  16. 16.
    Haralick, R.M.: Statistical and structural approaches to texture. Proc. IEEE 67(5), 786–804 (1979)CrossRefGoogle Scholar
  17. 17.
    Mansoor, A., et al.: A generic approach to pathological lung segmentation. IEEE Trans. Med. Imaging 33(12), 2293–2310 (2014)CrossRefGoogle Scholar
  18. 18.
    Artaechevarria, X., et al.: Longitudinal study of a mouse model of chronic pulmonary inflammation using breath hold gated micro-CT. Eur. Radiol. 20(11), 2600–2608 (2010)CrossRefGoogle Scholar
  19. 19.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  20. 20.
    Depeursinge, A., Foncubierta-Rodriguez, A., Van De Ville, D., Müller, H.: Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities. Med. Image Anal. 18(1), 176–196 (2014)CrossRefGoogle Scholar
  21. 21.
    Arganda-Carreras, I., et al.: Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics 33(15), 2424–2426 (2017)CrossRefGoogle Scholar
  22. 22.
    Laptev, D., Vezhnevets, A., Dwivedi, S., Buhmann, J.M.: Anisotropic ssTEM image segmentation using dense correspondence across sections, pp. 323–330 (2012)CrossRefGoogle Scholar
  23. 23.
    Villa, M.M., Wang, L., Huang, J., Rowe, D.W., Wei, M.: Visualizing osteogenesis in vivo within a cell-scaffold construct for bone tissue engineering using two-photon microscopy. Tissue Eng. Part C. Methods 19(11), 839–849 (2013)CrossRefGoogle Scholar
  24. 24.
    Frank, M., et al.: Mitophagy is triggered by mild oxidative stress in a mitochondrial fission dependent manner. Biochim. Biophys. Acta - Mol. Cell Res. 1823(12), 2297–2310 (2012)CrossRefGoogle Scholar
  25. 25.
    Anuranjeeta, A., Shukla, K.K., Tiwari, A., Sharma, S.: Classification of histopathological images of breast cancerous and non cancerous cells based on morphological features. Biomed. Pharmacol. J. 10(1), 353–366 (2017)CrossRefGoogle Scholar
  26. 26.
    Wollatz, L., Johnston, S.J., Lackie, P.M., Cox, S.J.: 3D Histopathology—a lung tissue segmentation workflow for microfocus x-ray-computed tomography scans. J. Digit. Imaging 30(6), 772–781 (2017)CrossRefGoogle Scholar
  27. 27.
    Zhan, L., Tang, J., Sun, M., Qin, C.: Animal models for tuberculosis in translational and precision medicine. Front. Microbiol. 8, 717 (2017)CrossRefGoogle Scholar
  28. 28.
    Meng, T., Lin, L., Shyu, M.-L., Chen, S.-C.: Histology image classification using supervised classification and multimodal fusion. In: 2010 IEEE International Symposium on Multimedia, pp. 145–152 (2010)Google Scholar
  29. 29.
    Gordaliza, P.M., Muñoz-Barrutia, A., Via, L.E., Sharpe, S., Desco, M., Vaquero, J.J.: Computed tomography-based biomarker for longitudinal assessment of disease burden in pulmonary tuberculosis. Mol. Imaging Biol. 1–6 (2018)Google Scholar
  30. 30.
    Thomsen, J.S., Laib, A., Koller, B., Prohaska, S., Mosekilde, L., Gowin, W.: Stereological measures of trabecular bone structure: comparison of 3D micro computed tomography with 2D histological sections in human proximal tibial bone biopsies. J. Microsc. 218(2), 171–179 (2005)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Particelli, F., Mecozzi, L., Beraudi, A., Montesi, M., Baruffaldi, F., Viceconti, F.: A comparison between micro-CT and histology for the evaluation of cortical bone: effect of polymethylmethacrylate embedding on structural parameters. J. Microsc. 245(3), 302–310 (2012)CrossRefGoogle Scholar
  32. 32.
    Xiao, G., et al.: Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer. Comput. Med. Imaging Graph. 35, 568–578 (2010)CrossRefGoogle Scholar
  33. 33.
    Bart, S., et al.: MRI-histology registration in prostate cancer. In: Proceedings of Surgetica, pp. 361–367 (2005)Google Scholar
  34. 34.
    Dullin, C., et al.: μCT of ex-vivo stained mouse hearts and embryos enables a precise match between 3D virtual histology, classical histology and immunochemistry. PLoS ONE 12(2), 1–15 (2017)CrossRefGoogle Scholar
  35. 35.
    Kak Slaney, M.A.C., et al.: Optimized murine lung preparation for detailed structural evaluation via micro-computed tomography. J. Appl. Phys. 12(3), 466–469 (2015)Google Scholar
  36. 36.
    Johnson, C., et al.: 3D human lung histology reconstruction and registration to in vivo imaging. In: Medical Imaging 2018: Digital Pathology, vol. 10581, p. 30 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ana Ortega-Gil
    • 1
    • 2
    Email author
  • Arrate Muñoz-Barrutia
    • 1
    • 2
  • Laura Fernandez-Terron
    • 1
  • Juan José Vaquero
    • 1
    • 2
  1. 1.Departamento de Bioingeniería e Ingeniería AeroespacialUniversidad Carlos III de MadridLeganésSpain
  2. 2.Instituto de Investigación Sanitaria Gregorio MarañónMadridSpain

Personalised recommendations