Computational Pathology

  • Peter J. Schüffler
  • Qing Zhong
  • Peter J. Wild
  • Thomas J. FuchsEmail author


Computational pathology offers a comprehensive framework for advanced study design in a wide range of research questions, as well as for standardized pipeline development for fast and reproducible computer-assisted routine diagnostics. This new field emerges at the border of pathology and computer science and shows high potential to revolutionize established workflows in research and clinic, since not only computational models get faster and more efficient than before but also since an incredible amount of training data is being generated in modern hospitals which is mandatory for the training of informed and validated models.

We review the field of computational pathology and illustrate on two research examples how it will contribute to an accurate, objective, and reproducible study design comprising informed data acquisition, advanced pattern recognition, and transparent model validation.


Convolutional Neural Network Clear Cell Renal Cell Carcinoma Memorial Sloan Kettering Cancer Center PTEN Deletion Nucleus Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Norbert Wey and Monika Bieri from University Hospital Zurich for their manifold support and their careful evaluation of the scanning statistics at the hospital. This chapter was partly funded through the NIH/NCI Cancer Center Support Grant P30 CA008748.


  1. 1.
    Fuchs TJ, Wild PJ, Moch H, Buhmann JM. Computational pathology analysis of tissue microarrays predicts survival of renal clear cell carcinoma patients. In: Proceedings of the international conference on Medical Image Computing and Computer-Assisted Intervention MICCAI, Lecture Notes in Computer Science, vol. 5242. Berlin: Springer; 2008. p. 1–8.Google Scholar
  2. 2.
    Fuchs TJ, Buhmann JM. Computational pathology: challenges and promises for tissue analysis. Comput Med Imaging Graph. 2011;35(78):515–30.CrossRefPubMedGoogle Scholar
  3. 3.
    Louis DN, Feldman M, Carter AB, Dighe AS, Pfeifer JD, Bry L, Almeida JS, Saltz J, Braun J, Tomaszewski JE, Gilbertson JR, Sinard JH, Gerber GK, Galli SJ, Golden JA, Becich MJ. Computational pathology: a path ahead. Arch Pathol Lab Med. 2016;140(1):41–50.CrossRefPubMedGoogle Scholar
  4. 4.
    Louis DN, Gerber GK, Baron JM, Bry L, Dighe AS, Getz G, Higgins JM, Kuo FC, Lane WJ, Michaelson JS, Le LP, Mermel CH, Gilbertson JR, Golden JA. Computational pathology: an emerging definition. Arch Pathol Lab Med. 2014;138(9):1133–8.CrossRefPubMedGoogle Scholar
  5. 5.
    Gurcan M, Boucheron L, Can A, Madabhushi A, Rajpoot N, Yener B. Histopathological image analysis: a review. IEEE Rev Biomed Eng. 2009;2:147–71.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Bautista PA, Hashimoto N, Yagi Y. Color standardization in whole slide imaging using a color calibration slide. J Pathol Inform. 2014;5(1):4.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Bautista PA, Yagi Y. Staining correction in digital pathology by utilizing a dye amount table. J Digit Imaging. 2015;28(3):283–94.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Giesen C, Wang HA, Schapiro D, Zivanovic N, Jacobs A, Hattendorf B, Schüffler PJ, Grolimund D, Buhmann JM, Brandt S, Varga Z, Wild PJ, Gunther D, Bodenmiller B. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11:417–22.CrossRefPubMedGoogle Scholar
  9. 9.
    Hashimoto N, Bautista PA, Haneishi H, Snuderl M, Yagi Y. Development of a 2d image reconstruction and viewing system for histological images from multiple tissue blocks: towards high-resolution whole-organ 3d histological images. Pathobiology. 2016;83(2–3):127–39.CrossRefPubMedGoogle Scholar
  10. 10.
    Balis U, Hipp J, Flotte T, Monaco J, Cheng J, Madabhushi A, Yagi Y, Rodriguez-Canales J, Emmert-Buck M, Dugan M, Hewitt S, Toner M, Tompkins R, Lucas D, Gilbertson J. Computer aided diagnostic tools aim to empower rather than replace pathologists: lessons learned from computational chess. J Pathol Inform. 2011;2(1):25.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Kramer BA, Gao X, Davis M, Hall M, Holzbeierlein J, Tawfik O. Prognostic significance of ploidy, MIB-1 proliferation marker, and p53 in renal cell carcinoma. J Am Coll Surg. 2005;201(4):565–70.CrossRefPubMedGoogle Scholar
  12. 12.
    Duda RO, Hart PE. Use of the Hough transformation to detect lines and curves in pictures. Commun ACM. 1972;15(1):11–5.Google Scholar
  13. 13.
    Schüffler PJ, Fuchs TJ, Ong CS, Roth V, Buhmann JM. Computational TMA analysis and cell nucleus classification of renal cell carcinoma. In: Proceedings of the 32nd DAGM conference on Pattern Recognition. Berlin: Springer; 2010. p. 202–11.Google Scholar
  14. 14.
    Schüffler PJ, Fuchs TJ, Ong CS, Wild P, Buhmann JM. TMARKER: a free software toolkit for histopathological cell counting and staining estimation. J Pathol Inform. 2013;4(2):2.CrossRefGoogle Scholar
  15. 15.
    Ruifrok AC, Johnston DA. Quantification of histochemical staining by color deconvolution. Anal Quant Cytol Histol. 2001;23:291–9.PubMedGoogle Scholar
  16. 16.
    Fuchs TJ, Haybaeck J, Wild PJ, Heikenwalder M, Moch H, Aguzzi A, Buhmann JM. Randomized tree ensembles for object detection in computational pathology. In: Proceedings of the 5th international symposium on Advances in Visual Computing: Part I, ISVC ’09. Berlin, Heidelberg, Las Vegas, Nevada: Springer; 2009. p. 367–78.Google Scholar
  17. 17.
    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44.CrossRefPubMedGoogle Scholar
  18. 18.
    Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, Martinez P, Matthews N, Stewart A, Tarpey P, Varela I, Phillimore B, Begum S, McDonald NQ, Butler A, Jones D, Raine K, Latimer C, Santos CR, Nohadani M, Eklund AC, Spencer-Dene B, Clark G, Pickering L, Stamp G, Gore M, Szallasi Z, Downward J, Futreal PA, Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Boykov Y, Veksler O, Zabih R. Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell. 2001;23(11):1222–39.CrossRefGoogle Scholar
  20. 20.
    Bosch A, Zisserman A, Munoz X. Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM international conference on Image and Video Retrieval, CIVR ’07. New York: ACM; 2007. p. 401–8.Google Scholar
  21. 21.
    Gonzalez RC, Woods RE, Eddins SL. Digital image processing using MATLAB. 2nd ed. Knoxville: Gatesmark Pub, S.I.; 2009.Google Scholar
  22. 22.
    Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.Google Scholar
  23. 23.
    Friedman J, Hastie T, Tibshirani R. The elements of statistical learning, Springer Series in Statistics, vol. 1. New York: Springer; 2001.Google Scholar
  24. 24.
    Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.CrossRefGoogle Scholar
  25. 25.
    Cohn DA, Ghahramani Z, Jordan MI. Active learning with statistical models. J Artif Intell Res. 1996;4(1):129–45.Google Scholar
  26. 26.
    Mahapatra D, Schüffler PJ, Tielbeek JAW, Makanyanga JC, Stoker J, Taylor SA, Vos FM, Buhmann JM. Active learning based segmentation of Crohn’s disease using principles of visual saliency. In: 2014 IEEE 11th international symposium on Biomedical Imaging (ISBI); 2014. p. 226–9.Google Scholar
  27. 27.
    Mahapatra D, Schüffler PJ, Tielbeek JAW, Vos FM, Buhmann JM. Semi-supervised and active learning for automatic segmentation of Crohn’s disease. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N, editors. Medical image computing and computer-assisted intervention, Lecture Notes in Computer Science, vol. 8150. Berlin: Springer; 2013. p. 214–21.Google Scholar
  28. 28.
    Schüffler PJ. Machine learning approaches for structure analysis in medical image data; 2014.Google Scholar
  29. 29.
    Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12:323–34.CrossRefPubMedGoogle Scholar
  30. 30.
    Swanton C. Intratumor heterogeneity: evolution through space and time. Cancer Res. 2012;72(19):4875–82.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    The Cancer Genome Atlas Research Network, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–20.CrossRefGoogle Scholar
  32. 32.
    Zhong Q, Busetto AG, Fededa JP, Buhmann JM, Gerlich DW. Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nat Methods. 2013;9:711–3.CrossRefGoogle Scholar
  33. 33.
    Zhong Q, Rüschoff JH, Guo T, Gabrani M, Schüffler PJ, Rechsteiner M, Liu Y, Fuchs TJ, Rupp NJ, Fankhauser C, Buhmann JM, Perner S, Poyet C, Blattner M, Soldini D, Moch H, Rubin MA, Noske A, Rüschoff J, Haffner MC, Jochum W, Wild PJ. Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity. Sci Rep. 2016;6:24146.CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Krohn A, Freudenthaler F, Harasimowicz S, Kluth M, Fuchs S, Burkhardt L, Stahl P, C Tsourlakis M, Bauer M, Tennstedt P, Graefen M, Steurer S, Sirma H, Sauter G, Schlomm T, Simon R, Minner S. Heterogeneity and chronology of pten deletion and erg fusion in prostate cancer. Mod Pathol. 2014;27:1612–20.CrossRefPubMedGoogle Scholar
  35. 35.
    Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, Vijver MJvd, West RB, Rijn Mvd, Koller D. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011;3(108):108ra113.CrossRefPubMedGoogle Scholar
  36. 36.
    Yuan Y, Failmezger H, Rueda OM, Ali HR, Grf S, Chin SF, Schwarz RF, Curtis C, Dunning MJ, Bardwell H, Johnson N, Doyle S, Turashvili G, Provenzano E, Aparicio S, Caldas C, Markowetz F. Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci Transl Med. 2012;4(157):157ra143.CrossRefPubMedGoogle Scholar
  37. 37.
    Noske A, Henricksen LA, LaFleur B, Zimmermann AK, Tubbs A, Singh S, Storz M, Fink D, Moch H. Characterization of the 19q12 amplification including {CCNE1} and {URI} in different epithelial ovarian cancer subtypes. Exp Mol Pathol. 2015;98(1):47–54.CrossRefPubMedGoogle Scholar
  38. 38.
    Zerhouni E, Prisacari B, Zhong Q, Wild P, Gabrani M. Deciphering protein signatures using color, morphological, and topological analysis of immunohistochemically stained human tissues. In Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 97910T (March 23, 2016); 2016.Google Scholar
  39. 39.
    Greenspan H, van Ginneken B, Summers RM. Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging. 2016;35(5):1153–9.CrossRefGoogle Scholar
  40. 40.
    Rampasek L, Goldenberg A. TensorFlow: biology’s gateway to deep learning? Cell Syst. 2016;2(1):12–4.CrossRefPubMedGoogle Scholar
  41. 41.
    Rueckert D, Glocker B, Kainz B. Learning clinically useful information from images: past, present and future. Med Image Anal. 2016;33:13–8.CrossRefPubMedGoogle Scholar
  42. 42.
    Zerhouni E, Prisacari B, Zhong Q, Wild P, Gabrani M. A computational framework for disease grading using protein signatures. In: 2016 IEEE 13th international symposium on Biomedical Imaging (ISBI); 2016. p. 1401–4.Google Scholar
  43. 43.
    Schüffler PJ, Sarungbam J, Muhammad H, Reznik E, Tickoo SK, Fuchs TJ. Mitochondria-based renal cell carcinoma subtyping: learning from deep vs. flat feature representations. J Mach Learn Res Proc. 2016.Google Scholar
  44. 44.
    Bauer S, Carion N, Schüffler P, Fuchs T, Wild P, Buhmann JM. Multi-organ cancer classification and survival analysis; 2016. arXiv:1606.00897 [cs, q-bio, stat].Google Scholar
  45. 45.
    Litjens G, Snchez CI, Timofeeva N, Hermsen M, Nagtegaal I, Kovacs I, Hulsbergen van de Kaa C, Bult P, van Ginneken B, van der Laak J. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep. 2016;6:26286.CrossRefPubMedPubMedCentralGoogle Scholar
  46. 46.
    Sirinukunwattana K, Raza SEA, Tsang YW, Snead DRJ, Cree IA, Rajpoot NM. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans Med Imaging. 2016;35(5):1196–206.CrossRefPubMedGoogle Scholar
  47. 47.
    Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, Madabhushi A. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images. IEEE Trans Med Imaging. 2016;35(1):119–30.CrossRefPubMedGoogle Scholar
  48. 48.
    Zhou Y, Chang H, Barner K, Spellman P, Parvin B. Classification of histology sections via multispectral convolutional sparse coding. Conf Comput Vis Pattern Recognit Workshops. 2014;2014:3081–8.PubMedPubMedCentralGoogle Scholar
  49. 49.
    Levenson RM, Krupinski EA, Navarro VM, Wasserman EA. Pigeons (Columba livia) as trainable observers of pathology and radiology breast cancer images. PLoS One. 2015;10(11):e0141357.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    Braxton M, Bruny T, Elmore J, Gagnon S, Raghunath V, Reisch L, Shapiro L, Weaver D, Allison K. Mouse cursor movement and eye tracking data as an indicator of pathologists attention when viewing digital whole slide images. J Pathol Inform. 2012;3(1):43.CrossRefPubMedPubMedCentralGoogle Scholar
  51. 51.
    Mercan E, Aksoy S, Shapiro LG, Weaver DL, Bruny TT, Elmore JG. Localization of diagnostically relevant regions of interest in whole slide images: a comparative study. J Digit Imaging. 2016;29(4):496–506.CrossRefPubMedGoogle Scholar
  52. 52.
    Schaumberg AJ, Sirintrapun SJ, Al-Ahmadie HA, Schüffler PJ, Fuchs TJ. Deep-scope: nonintrusive whole slide saliency annotation and prediction from pathologists at the microscope. In: 13th international conference on Computational Intelligence Methods for Bioinformatics and Biostatistics. Stirling, UK; 2016.Google Scholar
  53. 53.
    Nawaz S, Yuan Y. Computational pathology: exploring the spatial dimension of tumor ecology. Cancer Lett. 2016;380(1):296–303.CrossRefPubMedGoogle Scholar
  54. 54.
    Zhong Q, Barnert R, Ratsch G, Fuchs TJ, Wild PJ. Big Data in der Medizin. Leading Opinions: Hämatologie & Onkologie; 2016. p. 102–5.Google Scholar
  55. 55.
    Yagi Y, Riedlinger G, Xu X, Nakamura A, Levy B, Iafrate AJ, Mino-Kenudson M, Klepeis VE. Development of a database system and image viewer to assist in the correlation of histopathologic features and digital image analysis with clinical and molecular genetic information. Pathol Int. 2016;66(2):63–74.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Peter J. Schüffler
    • 1
  • Qing Zhong
    • 2
  • Peter J. Wild
    • 2
  • Thomas J. Fuchs
    • 1
    Email author
  1. 1.Memorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.Department of Pathology and Molecular PathologyUniversity Hospital ZurichZurichSwitzerland

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