Automatic quantification of the microvascular density on whole slide images, applied to paediatric brain tumours

  • Christophe Deroulers Univ Paris Diderot-Paris 7, Laboratoire IMNC, UMR 8165 CNRS, Univ Paris-Sud, F-91405 Orsay
  • Volodia Dangouloff-Ros Department of Paediatric Radiology, Hopital Necker Enfants Malades, AP-HP, F-75105 Paris, and INSERM U1000, Paris
  • Mathilde Badoual Univ Paris Diderot, Laboratoire IMNC, UMR 8165 CNRS, Univ Paris-Sud, F-91405 Orsay
  • Pascale Varlet Department of Neuropathology, Centre Hospitalier Sainte-Anne, Paris, and INSERM U1000, Paris, and Univ Paris Descartes, Paris
  • Nathalie Boddaert Department of Paediatric Radiology, Hopital Necker Enfants Malades, AP-HP, F-75105 Paris, and INSERM U1000, Paris, and Univ Paris Descartes, Paris, and UMR 1163, Institut Imagine, Paris



Angiogenesis is a key phenomenon for tumour progression, diagnosis and treatment in brain tumours and more generally in oncology. Presently, its precise, direct quantitative assessment can only be done on whole tissue sections immunostained to reveal vascular endothelial cells. But this is a tremendous task for the pathologist and a challenge for the computer since digitised whole tissue sections, whole slide mages (WSI), contain typically around ten gigapixels.


We define and implement an algorithm that determines automatically, on a WSI at objective magnification 40x, the regions of tissue, the regions without blur and the regions of large puddles of red blood cells, and constructs the mask of blur-free, significant tissue on the WSI. Then it calibrates automatically the optical density ratios of the immunostaining of the vessel walls and of the counterstaining, performs a colour deconvolution inside the regions of blur-free tissue, and finds the vessel walls inside these regions by selecting, on the image resulting from the colour deconvolution, zones which satisfy a double-threshold criterion. The two thresholds involved are automatically computed from the WSI so as to cope with variations in staining and digitisation parameters. A mask of vessel wall regions on the WSI is produced. The density of microvessels is finally computed as the fraction of the area of significant tissue which is occupied by vessel walls. We apply this algorithm to a set of 186 WSI of paediatric brain tumours from World Health Organisation grades I to IV.


The algorithm and its implementation are able to distinguish on the WSI the significant tissue and the vessel walls. The segmentations are of very good quality although the set of slides is very heterogeneous (in tumour type, in staining and digitisation parameters, and inside WSI themselves, where the tissue was often very fragmented). The computation time is of the order of a fraction of an hour for each WSI even though a modest desktop computer is used (a 2012 Mac mini) and the average size of WSI is 7 gigapixels. The computed microvascular density is found to be robust. We find that it strongly correlates with the tumour grade.


We have introduced a method of automatic segmentation of significant, blur-free tissue and of vessel walls, and of quantification of the density of microvessels, in WSI. We successfully tested it on a large variety of brain tumour tissue samples. This method requires no training and estimates automatically several important parameters of the segmentation. It is robust and can easily be applied to other tumour types and other stainings. It should improve the reproducibility of quantitative estimates in pathology while sparing the pathologist time and effort.


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1. Carmeliet P., Jain R.K., Angiogenesis in cancer and other diseases, Nature 2000, 407(6801):249–57.

2. Onishi M., Ichikawa T., Kurozumi K., Date I., Angiogenesis and invasion in glioma, Brain Tumor Pathol 2011, 28:13–24.

3. Plate K.H., Scholz A., Dumont D.J., Tumor angiogenesis and anti-angiogenic therapy in malignant gliomas revisited, Acta Neuropathol 2012, 124(6):763–75.

4. Louis D.N., Perry A., Reifenberger G., von Deimling A., Figarella-Branger D., Cavenee W.K., Ohgaki H., Wiestler O.D., Kleihues P., Ellison D.W., The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary, Acta Neuropathol 2016, 131(6):803–820.

5. Sun H., Xu Y., Yang Q., Wang W., Assessment of Tumor Grade and Angiogenesis in Colorectal Cancer: Whole-volume Perfusion CT, Acad Radiol 2014, 21(6):750–757.

6. Cha S., Knopp E.A., Johnson G., Wetzel S.G., Litt A.W., Zagzag D., Intracranial Mass Lesions: Dynamic Contrast-enhanced Susceptibility-weighted Echo-planar Perfusion MR Imaging, Radiology 2002, 223:11–29.

7. Peet A.C., Arvanitis T.N., Leach M.O., Waldman A.D., Functional imaging in adult and paediatric brain tumours, Nat Rev Clin Oncol 2012, 9:700–11.

8. Parums D.V., Cordell J.L., Micklem K., Heryet A.R., Gatter K.C., Mason D.Y., JC70: a new monoclonal antibody that detects vascular endothelium associated antigen on routinely processed tissue sections, J. Clin. Pathol. 1990, 43(9):752–757.

9. Fina L., Molgaard H., Robertson D., Bradley N., Monaghan P., Delia D., Sutherland D., Baker M., Greaves M., Expression of the CD34 gene in vascular endothelial cells, Blood 1990, 75(12):2417–2426.

10. Folkerth R.D., Descriptive analysis and quantification of angiogenesis in human brain tumors, J. Neurooncol. 2000, 50:165–72.

11. Dangouloff-Ros V., Deroulers C., Foissac F., Badoual M., Shotar E., Grévent D., Calmon R., Pagès M., Grill J., Dufour C., Blauwblomme T., Puget S., Zerah M., Sainte-Rose C., Brunelle F., Varlet P., Boddaert N., Arterial Spin Labeling to predict brain tumor grading in children: Correlations between histopathologic vascular density and perfusion MR Imaging, Radiology 2016, 281.

12. Kayser K., Nwoye J.O., Kosjerina Z., Goldmann T., Vollmer E., Kaltner H., André S., Gabius H.J., Atypical adenomatous hyperplasia of lung: its incidence and analysis of clinical, glycohistochemical and structural features including newly defined growth regulators and vascularization, Lung Cancer 2003, 42:171–182.

13. Gürcan M.N., Boucheron L.E., Can A., Madabhushi A., Rajpoot N.M., Yener B., Histopathological Image Analysis: A Review. Biomedical Engineering, IEEE Reviews in 2009, 2:147–171.

14. Kim N.T., Elie N., Plancoulaine B., Herlin P., Coster M., An Original Approach for Quantification of Blood Vessels on the Whole Tumour Section, Anal. Cell. Pathol. 2003, 25(2):63–75.

15. Françoise R., Michels J.J., Plancoulaine B., Herlin P., Optimal resolution for automatic quantification of blood vessels on digitized images of the whole cancer section, Image Anal Stereol 2005, 24:59–67.

16. Diamond J., McCleary D., Virtual Microscopy. In Advanced techniques in diagnostic cellular pathology. Edited by Hannon-Fletcher M., Maxwell P., Chichester, U.K., John Wiley & Sons, Ltd 2009.

17. Deroulers C., Ameisen D., Badoual M., Gerin C., Granier A., Lartaud M., Analyzing huge pathology images with open source software, Diagn. Pathol. 2013, 8:92.

18. Deroulers C., LargeTIFFTools, 2013–2016, [Available from:].

19. Deroulers C., NDPITools, 2011–2016, [Available from:].

20. Reyes-Aldasoro C., Williams L., Akerman S., Kanthou C., Tozer G., An automatic algorithm for the segmentation and morphological analysis of microvessels in immunostained histological tumour sections, J. Microsc. 2011, 242(3):262–278.

21. Niazi M.K.K., Hemminger J., Kurt H., Lozanski G., Gürcan M.N., Grading Vascularity from Histopathological Images based on Traveling Salesman Distance and Vessel Size, In Proc. SPIE, Volume 9041 2014:90410C–90410C–7.

22. Fernández-Carrobles M.M., Tadeo I., Noguera R., García-Rojo M., Déniz O., Salido J., Bueno G., A morphometric tool applied to angiogenesis research based on vessel segmentation. Diagn. Pathol. 2013, 8(Suppl 1):S20.

23. Morin K., Carlson P., Tranquillo R., Automated image analysis programs for the quantification of microvascular network characteristics, Methods 2015, 84:76–83.

24. Kayser K., Introduction of virtual microscopy in routine surgical pathology — a hypothesis and personal view from Europe, Diagnostic pathology 2012, 7:48.

25. BigTIFF Design, 2012, [Available from:].

26. Goode A., Satyanarayanan M., A Vendor-Neutral Library and Viewer for Whole-Slide Images, Tech. Rep. Technical Report CMU-CS-08-136, Computer Science Department, Carnegie Mellon University 2008, [Available from:].

27. Goode A., Gilbert B., Harkes J., Jukic D., Satyanarayanan M., OpenSlide: A Vendor-Neutral Software Foundation for Digital Pathology, J. Pathol. Inform. 2013, 4:27.

28. Lane T.G., Vollbeding G., The Independent JPEG Group’s JPEG software, 2013, [Available from:].

29. Sam Leffler S., the authors of LibTIFF: LibTIFF – TIFF Library and Utilities, 2012, [Available from:].

30. Ameisen D, Deroulers C, Perrier V, Yunès JB, Battistella M, Bouhidel F, Legrès L, Janin A, Bertheau P: Stack or Trash? Quality assessment of virtual slides. Diagnostic Pathology 2013, 8(Suppl 1):S23.

31. Ameisen D., Intégration des lames virtuelles dans le dossier patient électronique, PhD thesis, Univ Paris Diderot-Paris 7 2013.

32. Martinez K., Cupitt J., VIPS - a highly tuned image processing software architecture. In Proc. IEEE International Conference on Image Processing 2 2005:574–577.

33. Rasband W.S.: ImageJ, 1997–2016, [Available from:].

34. Schneider C.A., Rasband W.S., Eliceiri K.W., NIH Image to ImageJ: 25 years of image analysis, Nature Methods 2012, 9:671–675.

35. Ridler T.W., Calvard S., Picture Thresholding Using an Iterative Selection Method, IEEE Transactions on Systems, Man, and Cybernetics 1978, 8(8):630–632.

36. Ruifrok A., Johnston D., Quantification of Histochemical Staining by Color Deconvolution, Anal. Quant. Cyt. Hist. 2001, 23:291–299.

37. CodePlex Foundation, OpenSeadragon contributors: OpenSeadragon, 2015, [Available from:].

38. Sharma H., Zerbe N., Lohmann S., Kayser K., Hellwich O., Hufnagl P., A review of graph-based methods for image analysis in digital histopathology, Diagnostic Pathology 2016, 1:61.

39. Badoual M., Gerin C., Deroulers C., Grammaticos B., Llitjos J.F., Oppenheim C., Varlet P., Pallud J., Oedema-based model for diffuse low-grade gliomas: application to clinical cases under radiotherapy. Cell Proliferation 2014, 47(4):369–380.

40. Kayser K., Borkenfeld S., Carvalho R., Djenouni A., Kayser G., How to analyze Structure and Function in Tissue - based Diagnosis?, Diagnostic Pathology 2016, 2:106.

41. Labiche A., Elie N., Herlin P., Denoux Y., Crouet H., Heutte N., Joly F., Héron J.F., Gauduchon P., Henry-Amar M., Prognostic significance of tumor vascularisation on survival of patients with advanced ovarian carcinoma, Histol Histopathol 2009, 24:425–435.

42. Konda V.J.A., Hart J., Lin S., Tretiakova M., Gordon I.O., Campbell L., Kulkarni A., Bissonnette M., Seewald S., Waxman I., Evaluation of microvascular density in Barrett’s associated neoplasia, Modern Pathology 2013, 26:125–130.

43. Szöke T., Kayser K., Trojan I., Kayser G., Furak J., Tiszlavicz L., Baumhäkel J.D., Gabius H.J., The role of microvascularization and growth/adhesion-regulatory lectins in the prognosis of non-small cell lung cancer in stage II. European Journal of Cardio-Thoracic Surgery 2007, 31(5):783–787.
How to Cite
DEROULERS, Christophe et al. Automatic quantification of the microvascular density on whole slide images, applied to paediatric brain tumours. Diagnostic Pathology, [S.l.], v. 2, n. 1, sep. 2016. ISSN 2364-4893. Available at: <>. Date accessed: 26 nov. 2020. doi:


Digital Pathology; Image Processing; Whole Slide Images; Angiogenesis; Microvessels; Brain Tumour