Abstract
An increasingly common component of molecular diagnostics is the analysis of protein localization at the single-cell level using fluorescent microscopy. Manually extracting quantitative data from a large population of cells is unreasonably time-consuming and existing automatic systems are rather limited.
Here we present an integrated image analysis software system for high-throughput segmentation of cells and corresponding nuclei. The system is composed of robust image enhancement, followed by multispectral identification of putative cells using a statistical model (Gaussian Mixture Model) approach, followed by cross-spectral watershed that effectively segments clustered cells, and finally, a rule based refinement using statistical morphological attributes of the cells.
The robustness and accuracy of the system have been tested on artificial fluorescent beads, as well as on hand segmented and visually inspected images. Lastly, we compare our algorithm to state-of-the-art systems and show it does better on most performance parameters.
To date, the system has been used to accumulate data from over 300 million segmented cells each expressing specific set of genomic alterations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bleau, A., Leon, J.L.: Watershed-based segmentation and region merging. Comput. Vis. Image Underst. 77(3), 317–370 (2000)
Brown, R.G., Hwang, P.Y.: Introduction to Random Signals and Applied Kalman Filtering. Wiley, New York (1997)
Brown, C.M.: Fluorescence microscopy-avoiding the pitfalls. J. Cell Sci. 120(10), 1703–1705 (2007)
Bright, D.S., Steel, E.B.: Two-dimensional top hat filter for extracting spots and spheres from digital images. J. Microsc. 146(2), 191–200 (1987)
Coelho, L.P., Shariff, A., Murphy, R.F.: Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms. In: Proceedings/IEEE International Symposium on Biomedical Imaging: from Nano to Macro IEEE International Symposium on Biomedical Imaging, vol. E6, pp. 518–521 (2009). doi:10.1109/ISBI.2009.5193098
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, Hoboken (1973)
Le Tourneau, C., et al.: Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer SHIVA: a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 16(13), 1324–1334 (2015)
Lindblad, J., Wahlby, C., Bengtsson, E., Zaltman, A.: Image analysis for automatic segmentation of cytoplasm and classification of Rac1 activation. Cytometry 57A, 22–33 (2004)
Otsu, N.: A threshold selection method from gray level histogram? IEEE Trans. Syst. Man Cybern. SMC–8, 62–66 (1978)
Tarnowski, B.I., Spinale, F.G., Nicholson, J.H.: DAPI as a useful stain for nuclear quantitation. Biotech. Histochem. 66(6), 296–302 (1991)
Vogelstein, B., et al.: Cancer genome landscapes. Science 339(6127), 1546–1558 (2013)
Chen, X., Zhou, X., Wong, S.T.C.: Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans. Biomed. Eng. 53(4), 762–766 (2006)
Lim, J.S.: Two-Dimensional Signal and Image Processing. Prentice Hall, Englewood Cliffs (1990). Equations 9.26, 9.27, and 9.29
Poynton, C.: Digital Video and HDTV Algorithms and Interfaces. Morgan Kaufman Publishers, San Francisco (2003)
Turski, M.L., et al.: Genomically driven tumors and actionability across histologies: BRAF-mutant cancers as a paradigm. Mol. Cancer Ther. 15, 533–547 (2016)
Friedman, A.A., Letai, A., Fisher, D.E., Flaherty, K.T.: Precision medicine for cancer with next-generation functional diagnostics. Nat. Rev. Cancer 15, 747–756 (2015)
Chapman, P.B., et al.: Improved survival with vemurafenib in melanoma with BRAF-V600E mutation. N. Engl. J. Med. 364, 2507–2516 (2011)
Acknowledgments
The authors would like to gratefully acknowledge the entire NovellusDx team which run the biological experiments and the deep discussions with regards to biological parameters to exclude cells. The authors would also like to thank Prof Zohar Yakhini for his careful review and insightful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Golbstein, J., Tocker, Y., Sharivkin, R., Tarcic, G., Vidne, M. (2017). A Novel High-Throughput Multispectral Cell Segmentation Algorithm. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_66
Download citation
DOI: https://doi.org/10.1007/978-3-319-60964-5_66
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60963-8
Online ISBN: 978-3-319-60964-5
eBook Packages: Computer ScienceComputer Science (R0)