Epithelial Cell Segmentation via Shape Ranking

  • Alberto Santamaria-PangEmail author
  • Yuchi Huang
  • Zhengyu Pang
  • Li Qing
  • Jens Rittscher
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 14)


We present a robust and high-throughput computational method for cell segmentation using multiplexed immunohistopathology images. The major challenges in obtaining an accurate cell segmentation from tissue samples are due to (i) complex cell and tissue morphology, (ii) different sources of variability including non-homogeneous staining and microscope specific noise, and (iii) tissue quality. Here we present a fast method that uses cell shape and scale information via unsupervised machine learning to enhance and improve general purpose segmentation methods. The proposed method is well suited for tissue cytology because it captures the the morphological and shape heterogeneity in different cell populations. We discuss our segmentation framework for analysing approximately one hundred images of lung and colon cancer and we restrict our analysis to epithelial cells.


Markov Random Field Shape Descriptor Nottingham Prognostic Index Cell Segmentation Watershed Algorithm 
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.



This work has been developed as part of a larger interdisciplinary research program led by Fiona Ginty. In particular we would like to thank Michael Gerdes, Anup Sood, Christopher Sevinsky, and Brian Sarachan for valuable feedback. Without their collaboration it would have not been possible to evaluate the cell segmentation framework on such vast array of tissue samples. Throughout these studies they have guided our thinking on how more robust and reliable methods could be developed. This work was performed while Yuchi Huang was in GE Global Research.


  1. 1.
    Society AC (2013) Cancer facts and figures 2013. Technical report, Atlanta, GAGoogle Scholar
  2. 2.
    Boyle P, Levin B (eds) (2008) World cancer report 2008. IARC Nonserial, GenevaGoogle Scholar
  3. 3.
    Fuller C, Straight A (2010) Image analysis benchmarking methods for high-content screen design. J Microsc 238(2):145–161CrossRefMathSciNetGoogle Scholar
  4. 4.
    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 RG, Lucas D, Gilbertson JR, Balis U (2011) Computer aided diagnostic tools aim to empower rather than replace pathologists: lessons learned from computational chess. J Pathol Inf 2(1):25CrossRefGoogle Scholar
  5. 5.
    Mccullough B, Ying X, Monticello T, Bonnefoi M (2004) Digital microscopy imaging and new approaches in toxicologic pathology. Toxicol Pathol 32(2 suppl):49–58CrossRefGoogle Scholar
  6. 6.
    Meijering E (2012) Cell segmentation: 50 years down the road. IEEE Signal Process Mag 29(5):140–145CrossRefGoogle Scholar
  7. 7.
    Fuchs TJ, Buhmann JM (2011) Computational pathology: challenges and promises for tissue analysis. Comput Med Imaging Graph 35(78): 515–530Google Scholar
  8. 8.
    Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171CrossRefGoogle Scholar
  9. 9.
    Gleason DF, Mellinger GT (1974) Prediction of prognosis for prostatic adenocarcinoma by combined histological grading and clinical staging. J Urol 111(1):58Google Scholar
  10. 10.
    Galea MH, Blamey RW, Elston CE, Ellis IO (1992) The nottingham prognostic index in primary breast cancer. Breast Cancer Res Treat 22(3):207–219CrossRefGoogle Scholar
  11. 11.
    Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, West RB, van de Rijn M, Koller D (2011) Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Science Transl Med 3(108): 108ra113Google Scholar
  12. 12.
    Ginty F, Adak S, Can A, Gerdes M, Larsen M, Cline H, Filkins R, Pang Z, Li Q, Montalto MC (2008) The relative distribution of membranous and cytoplasmic met is a prognostic indicator in stage i and ii colon cancer. Clin Cancer Res 14(12):3814–3822CrossRefGoogle Scholar
  13. 13.
    Gerdes MJ, Sevinsky CJ, Sood A, Adak S, Bello MO, Bordwell A, Can A, Corwin A, Dinn S, Filkins RJ, Hollman D, Kamath V, Kaanumalle S, Kenny K, Larsen M, Lazare M, Li Q, Lowes C, McCulloch CC, McDonough E, Montalto MC, Pang Z, Rittscher J, Santamaria-Pang A, Sarachan BD, Seel ML, Seppo A, Shaikh K, Sui Y, Zhang J, Ginty F (2013) Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. In: Proc Natl Acad Sci, 110(29):11982–11987, 2013Google Scholar
  14. 14.
    Nelson DA, Manhardt C, Kamath V, Sui Y, Santamaria-Pang A, Can A, Bello M, Corwin A, Dinn SR, Lazare M, Gervais EM, Sequeira SJ, Peters SB, Ginty F, Gerdes MJ, Larsen M (2013) Quantitative single cell analysis of cell population dynamics during submandibular salivary gland development and differentiation. Biol Open 2(5):439–447CrossRefGoogle Scholar
  15. 15.
    Monaco JP, Tomaszewski JE, Feldman MD, Hagemann I, Moradi M, Mousavi P, Boag A, Davidson C, Abolmaesumi P, Madabhushi A (2010) High-throughput detection of prostate cancer in histological sections using probabilistic pairwise Markov models. Med Image Anal 14(4):617–629CrossRefGoogle Scholar
  16. 16.
    Basavanhally AN, Ganesan S, Agner S, Monaco JP, Feldman MD, Tomaszewski JE, Bhanot G, Madabhushi A (2010) Computerized image-based detection and grading of lymphocytic infiltration in her2+ breast cancer histopathology. IEEE Trans Biomed Eng 57(3):642–653CrossRefGoogle Scholar
  17. 17.
    Kaynig V, Fuchs T, Buhmann JM (2010) Neuron geometry extraction by perceptual grouping in ssTEM images. In: 2010 IEEE computer society conference on computer vision and pattern recognition, June 2010, pp 2902–2909Google Scholar
  18. 18.
    Doyle S, Feldman M, Tomaszewski J, Madabhushi A (2012) A boosted bayesian multiresolution classifier for prostate cancer detection from digitized needle biopsies. IEEE Trans Biomed Eng 59(5):1205–1218CrossRefGoogle Scholar
  19. 19.
    Doyle, S., Feldman, M., Tomaszewski, J., Shih, N., Madabhushi, A (2011) Cascaded multi-class pairwise classifier (cascampa) for normal, cancerous, and cancer confounder classes in prostate histology. In: 2011 IEEE international symposium on biomedical imaging: from nano to macro, pp 715–718Google Scholar
  20. 20.
    Chang J, Arbelez PA, Switz N, Reber C, Tapley A, Davis JL, Cattamanchi A, Fletcher D, Malik J (2012) Automated tuberculosis diagnosis using fluorescence images from a mobile microscope. In: Ayache N, Delingette H, Golland P, Mori K (eds) MICCAI (3), Volume 7512 of Lecture notes in computer science. Springer, pp 345–352Google Scholar
  21. 21.
    Nandy K, Gudla PR, Amundsen R, Meaburn KJ, Misteli T, Lockett SJ (2012) Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images. Cytometry Part A J Int Soc Anal Cytol 81(9):743–754CrossRefGoogle Scholar
  22. 22.
    Roullier V, Lzoray O, Ta VT, Elmoataz A (2011) Multi-resolution graph-based analysis of histopathological whole slide images: application to mitotic cell extraction and visualization. Comput Med Imaging Graph 35(78): 603–615 ¡ce:title¿Whole Slide Image Process¡/ce:title¿Google Scholar
  23. 23.
    Nath SK, Palaniappan K, Bunyak F (2006) Cell segmentation using coupled level sets and graph-vertex coloring. Med Image Comput Computer-Assist Interv 9(Pt 1):101–108Google Scholar
  24. 24.
    Lin G, Chawla MK, Olson K, Barnes CA, Guzowski JF, Bjornsson C, Shain W, Roysam B (2007) A multi-model approach to simultaneous segmentation and classification of heterogeneous populations of cell nuclei in 3D confocal microscope images. Cytometry Part A 71A:724–736CrossRefGoogle Scholar
  25. 25.
    Ta VT, Lzoray O, Elmoataz A, Schpp S (2009) Graph-based tools for microscopic cellular image segmentation. Pattern Recognit 42(6):1113–1125CrossRefGoogle Scholar
  26. 26.
    Keuper M, Schmidt T, Rodriguez-Franco M, Schamel W, Brox T, Burkhardt H, Ronneberger O (2011) Hierarchical Markov random fields for mast cell segmentation in electron microscopic recordings. In: IEEE international symposium on biomedical, imaging, pp 973–978Google Scholar
  27. 27.
    Can A, Bello M, Gerdes M (2010) Quantification of subcellular molecules in tissue microarray. In: 2010 20th international conference on pattern recognition (ICPR), pp 2548–2551Google Scholar
  28. 28.
    Can A, Bello M, Cline HE, Tao X, Mendonca P, Gerdes M (2009) A unified segmentation method for detecting subcellular compartments in immuno-fluorescently labeled tissue images. In: Proceedings of the 2009 international workshop in microscopy image analysis with applications in biology, Bethesda, MD, Sept 2009Google Scholar
  29. 29.
    Cukierski WJ, Nandy K, Gudla PR, Meaburn KJ, Misteli T, Foran DJ, Lockett SJ (2012) Ranked retrieval of segmented nuclei for objective assessment of cancer gene repositioning. BMC Bioinform 13:232CrossRefGoogle Scholar
  30. 30.
    Arslan S, Ersahin T, Cetin-Atalay R, Gunduz-Demir C (2013) Attributed relational graphs for cell nucleus segmentation in fluorescence microscopy images. IEEE Trans Med Imaging 32(6):1121–1131CrossRefGoogle Scholar
  31. 31.
    Lou X, Koethe U, Wittbrodt J, Hamprecht FA (2012) Learning to segment dense cell nuclei with shape prior. In: 2012 IEEE conference on computer vision and, pattern recognition, June 2012, pp 1012–1018Google Scholar
  32. 32.
    Bamford P (2003) Empirical comparison of cell segmentation algorithms using an annotated dataset. In: Proceedings of the 2003 international conference on image processing, 2003 (ICIP 2003), vol 2, Sept 2003, II - 1073–6 vol. 3Google Scholar
  33. 33.
    Na S, Heru X (2009) The segmentation of overlapping milk somatic cells based on improved watershed algorithm. In: International conference on artificial intelligence and computational intelligence, AICI ’09, vol 3, Nov 2009, pp 563–566Google Scholar
  34. 34.
    Srinivasa G, Fickus M, Guo Y, Linstedt A, Kovacevic J (2009) Active mask segmentation of fluorescence microscope images. IEEE Trans Image Process 18(8):1817–1829CrossRefMathSciNetGoogle Scholar
  35. 35.
    Xiao Y, Pham T, Chang J, Zhou X (2011) Symmetry-based presentation for stem-cell image segmentation. In: 2011 IEEE 1st international conference on computational advances in bio and medical sciences (ICCABS), Feb 2011, pp 196–201Google Scholar
  36. 36.
    Martinez G, Frerichs JG, Scheper T (2011) Bubble segmentation based on shape from shading for in-situ microscopy. In: 2011 21st international conference on electrical communications and computers (CONIELECOMP), 28 Feb 2011–2 March 2011, p 1–4Google Scholar
  37. 37.
    Kong H, Gurcan M, Boussaid K (2011) Partitioning histopathological images: an integrated framework for supervised color-texture segmentation and cell splitting. IEEE Trans Med Imaging PP(99):1Google Scholar
  38. 38.
    Xiong W, Wang Y, Ong S, Lim JH, Jiang L (2010) Learning cell geometry models for cell image simulation: an unbiased approach. In: 2010 17th IEEE international conference on image processing (ICIP), Sept 2010, pp 1897–1900Google Scholar
  39. 39.
    Park M, Jin J, Peng Y, Summons P, Yu D, Cui Y, Luo S, Wang F, Santos L, Xu M (2010) Automatic cell segmentation in microscopic color images using ellipse fitting and watershed. In: 2010 IEEE/ICME international conference on complex medical engineering (CME), July 2010, pp 69–74Google Scholar
  40. 40.
    Zacharia E, Maroulis D (2010) 3-D spot modeling for automatic segmentation of cDNA microarray images. IEEE Trans Nanobiosci 9(3):181–192Google Scholar
  41. 41.
    Lempitsky V, Zisserman A (2010) Learning to count objects in images. Adv Neural Inf Process Syst 23:1324–1332Google Scholar
  42. 42.
    Arteta C, Lempitsky V, Noble J, Zisserman A (2012) Learning to detect cells using non-overlapping extremal regions. In: Medical image computing and computer assisted intervention, pp 348–356Google Scholar
  43. 43.
    Sood A, Montalto M, Gerdes M (2009) Sequential analysis of biological samples. USPTO Application #11/560,599. General Electric Company, United States of AmericaGoogle Scholar
  44. 44.
    Gerdes MJ, Ginty F, Larsen M, Montalto M, Pang Z, Sood A (2010) Sequential analysis of biological samples. USPTO Patent #7741045. General Electric Company, United States of AmericaGoogle Scholar
  45. 45.
    Larsen M, Sood A, Gerdes M, Montalto M, Pang Z, Ginty F (2010) Sequential analysis of biological samples. USPTO Application #11/864093. General Electric Company, United States of AmericaGoogle Scholar
  46. 46.
    Treynor T, Sood A, Gerdes M, Pang Z (2012) Sequential analysis of biological samples. General Electric Company, United States of AmericaGoogle Scholar
  47. 47.
    Rittscher J (2010) Characterization of biological processes through automated image analysis. Annu Rev Biomed Eng 12:315–44Google Scholar
  48. 48.
    Santamaria-Pang A, Huang Y, Rittscher J (2013) Cell segmentation and classification via unsupervised shape ranking. In: 10th IEEE international symposium on biomedical imaging: from nano to macro, 2013, (ISBI 2013). San Francisco, CA, April 2013Google Scholar
  49. 49.
    Mori G, Belongie S, Malik J (2005) Efficient shape matching using shape contexts. IEEE Trans Pattern Anal Mach Intell 27(11):1832–1837Google Scholar
  50. 50.
    Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, CVPR’2005Google Scholar
  51. 51.
    Can A, Bello M, Cline H, Tao X, Mendonca P, Gerdes M (2009) A unified segmentation method for detecting subcellular compartments in immunofluorescently labeled tissue images. In: Microscopic image analysis with applications in biologyGoogle Scholar
  52. 52.
    Kittler J, Illingworth J (1986) Minimum error thresholding. Pattern Recogn 19(1):41–47CrossRefGoogle Scholar
  53. 53.
    Frangi AF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: MICCAI, Springer, pp 130–137Google Scholar
  54. 54.
    Ibanez L, Schroeder W, Ng L, Cates J (2005) The ITK software guide, 2nd edn. Kitware, Inc. ISBN 1-930934-15-7,
  55. 55.
    Stewart E, Rittscher J, Sebastian TB (2007) Automatic characterization of cellular motion. US Patent #20080304732. General Electric Company, United States of AmericaGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alberto Santamaria-Pang
    • 1
    Email author
  • Yuchi Huang
    • 1
  • Zhengyu Pang
    • 1
  • Li Qing
    • 1
  • Jens Rittscher
    • 1
  1. 1.GE Global ResearchNiskayunaUSA

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