DOI QR코드

DOI QR Code

Automatic Segmentation of Cellular Images for High-Throughput Genome-Wide RNA Interference Screening

고속 Genome-Wide RNA 간섭 스크리닝을 위한 세포영상의 자동 분할

  • Received : 2010.02.17
  • Accepted : 2010.04.13
  • Published : 2010.04.28

Abstract

In recent years, high-throughput genome-wide RNA interference screening is emerging as an essential tool to biologists in understanding complex cellular processes. The manual analysis of the large number of images produced in each study spends much time and the labor. Hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. However, those factors such as the region overlapping, a variety of shapes, and non-uniform local characteristics of cellular images become obstacles to efficient cell segmentation. To avoid the problem, a new watershed-based cell segmentation algorithm using a localized segmentation method and a feature vector is proposed in this paper. Localized approach in segmentation resolves the problems caused by a variety of shapes and non-uniform characteristics. In addition, the poor performance of segmentation in overlapped regions can be improved by taking advantage of a feature vector whose component features complement each other. Simulation results show that the proposed method improves the segmentation performance compared to the method in Cellprofiler.

Keywords

High-Throughput Genome-Wide RNA Interference Screening;Nuclei Extraction;Cell Segmentation

References

  1. H. Peng, "Bioimage informatics: a new area engineering biology," Bioinformatics, Vol.24, No.17, pp.1827-1836, 2008. https://doi.org/10.1093/bioinformatics/btn346
  2. G. Lin, M. K. Chawla, K. Olson, J. F. Guzowski, C. A. Barnes, and B. Roysam, "Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei," Cytometry Part A, Vol.63, No.1, pp.23-33. 2005. https://doi.org/10.1002/cyto.a.20099
  3. G. Lin, U. Adiga, K. Olson, J. F. Guzowski, C. A. Barnes, and B. Roysam, "A hybrid 3-d watershed algorithm incorporating gradient cues & object models for automatic segmentation of nuclei in confocal image stacks," Cytometry Prat A, Vol.56, No.1, pp.23-36, 2003.
  4. M. Hu, X. Ping, and Y. Ding, "Automated cell nucleus segmentation using improved snake," International Conference on Image Processing, Vol.4, pp.2737-2740, 2004. https://doi.org/10.1109/ICIP.2004.1421670
  5. P. Bamford and B. Lovell, "Unsupervised Cell Nucleus Segmentation with Active Contours," Signal Processing, Vol.71, No.2, pp.203-213, 1998. https://doi.org/10.1016/S0165-1684(98)00145-5
  6. G. Cong and B. Parvin, "Model-based segmentation of nuclei," Pattern Recognition, Vol.33, No.8, pp.1383-1393, 2000. https://doi.org/10.1016/S0031-3203(99)00119-3
  7. T. Mouroutis, S. J. Roberts, and A. Bharath, "Robust cell nuclei segmentation using statistical modelling," Bioimaging Vol.6, No.2, pp.79-91, 1998. https://doi.org/10.1002/1361-6374(199806)6:2<79::AID-BIO3>3.0.CO;2-#
  8. G. Begelman, E. Gur, E. Rivlin, M. Rudzsky, and Z. Zalevsky, "Cell nuclei segmentation using fuzzy logic engine," International Conference on Image Processing, Vol.5, pp.2937-2940, 2004. https://doi.org/10.1109/ICIP.2004.1421728
  9. P. Yan, X. Zhou, M. Shah, and S. T. C. Wong, "Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images," IEEE Transactions on Information Technology in Biomedicine, Vol.12, No.1, pp.109-117, 2008. https://doi.org/10.1109/TITB.2007.898006
  10. M. R. Lamprecht, D. M. Sabatini, and A. E. Carpenter, "Cellprofiler : free, versatile software for automated biological image analysis," BioTechniques, Vol.42, No.1, 2007.
  11. N. Otsu, "A threshold selection method from gray level histograms," IEEE Transactions on Systems, Man and Cybernetics, Vol.9, No.1, 1979. https://doi.org/10.1109/TSMC.1979.4310076

Acknowledgement

Supported by : 한국연구재단