Cell Detection and Segmentation Using Correlation Clustering

  • Chong Zhang
  • Julian Yarkony
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Cell detection and segmentation in microscopy images is important for quantitative high-throughput experiments. We present a learning-based method that is applicable to different modalities and cell types, in particular to cells that appear almost transparent in the images. We first train a classifier to detect (partial) cell boundaries. The resulting predictions are used to obtain superpixels and a weighted region adjacency graph. Here, edge weights can be either positive (attractive) or negative (repulsive). The graph partitioning problem is then solved using correlation clustering segmentation. One variant we newly propose here uses a length constraint that achieves state-of-art performance and improvements in some datasets. This constraint is approximated using non-planar correlation clustering. We demonstrate very good performance in various bright field and phase contrast microscopy experiments.


  1. 1.
    Andres, B., Kappes, J.H., Beier, T., Köthe, U., Hamprecht, F.A.: Probabilistic Image Segmentation with Closedness Constraints. In: ICCV (2011)Google Scholar
  2. 2.
    Andres, B., Yarkony, J., Manjunath, B.S., Kirchhoff, S., Turetken, E., Fowlkes, C.C., Pfister, H.: Segmenting Planar Superpixel Adjacency Graphs w.r.t. Non-planar Superpixel Affinity Graphs. In: Heyden, A., Kahl, F., Olsson, C., Oskarsson, M., Tai, X.-C. (eds.) EMMCVPR 2013. LNCS, vol. 8081, pp. 266–279. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Arteta, C., Lempitsky, V., Noble, J.A., Zisserman, A.: Learning to Detect Cells Using Non-overlapping Extremal Regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Bachrach, Y., Kohli, P., Kolmogorov, V., Zadimoghaddam, M.: Optimal Coalition Structure Generation in Cooperative Graph Games. In: AAAI (2013)Google Scholar
  5. 5.
    Kappes, J.H., Andres, B., Hamprecht, F.A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B.X., Lellmann, J., Komodakis, N., Rother, C.: A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. In: CVPR (2013)Google Scholar
  6. 6.
    Kim, S., Nowozin, S., Kohli, P., Yoo, C.D.: Higher-Order Correlation Clustering for Image Segmentation. In: NIPS (2011)Google Scholar
  7. 7.
    Kvarnstrom, M., Logg, K., Diez, A., Bodvard, K., Kall, M.: Image Analysis Algorithms for Cell Contour Recognition in Budding Yeast. Opt. Express 16(17), 1035–1042 (2008)CrossRefGoogle Scholar
  8. 8.
    Mayer, C., Dimopoulos, S., Rudolf, F., Stelling, J.: Using CellX to Quantify Intracellular Events. Curr. Protoc. Mol. Biol., Chapter 14, Unit 14.22 (2013)Google Scholar
  9. 9.
    Peng, J.Y., Chen, Y.J., Green, M.D., Sabatinos, S.A., Forsburg, S.L., Hsu, C.N.: PombeX: Robust Cell Segmentation for Fission Yeast Transillumination Images. PLoS One 8(12), e81434 (2013)Google Scholar
  10. 10.
    Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: Ilastik: Interactive Learning and Segmentation Toolkit. In: ISBI (2011)Google Scholar
  11. 11.
    Yarkony, J., Ihler, A., Fowlkes, C.C.: Fast Planar Correlation Clustering for Image Segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI. LNCS, vol. 7577, pp. 568–581. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chong Zhang
    • 1
  • Julian Yarkony
    • 2
  • Fred A. Hamprecht
    • 2
  1. 1.CellNetworksHeidelberg UniversityGermany
  2. 2.HCI/IWRHeidelberg UniversityGermany

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