Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images

Abstract

GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.

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References

  1. Basu, S., Condron, B., Aksel, A., & Acton, S.T. (2013). Segmentation and tracing of single neurons from 3D confocal microscope images. IEEE Journal of Biomedical and Health Informatics, 17, 319–335.

    Article  PubMed  Google Scholar 

  2. Cardona, A., Saalfeld, S., Arganda, I., Pereanu, W., Schindelin, J., & Hartenstein, V. (2010). Identifying neuronal lineages of drosophila by sequence analysis of axon tracts. Journal of Neuroscience, 30(22), 7538–7553.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. Costa, M., Ostrovsky, A.D., Manton, J.D., Prohaska, S., & Jefferis, G.S. (2014). Nblast: Rapid, sensitive comparison of neuronal structure and construction of neuron family databases. bioRxiv.

  4. Coster, A.D., Wichaidit, C., Rajaram, S., Altschuler, S.J., & Wu, L.F. (2014). A simple image correction method for high-throughput microscopy. Nature Methods, 11(6), 602.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Dumouchel, W., & O’Brien, F. (1991). Integrating a robust option into a multiple regression computing environment. In Computing and Graphics in Statistics: Springer.

  6. Dzyubak, O.P., & Ritman, E.L. (2011). Automation of Hessian-based tubularity measure response function in 3D biomedical images. International Journal of Biomedical Imaging, 2011, 1–16.

  7. Frangi, A.F., Niessen, W.J., Vincken, K.L., & Viergever, M.A. (1998). Multiscale vessel enhancement filtering. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), (Vol. 1496 pp. 130–137): Springer Science + Business Media.

  8. Ganglberger, F., Schulze, F., Tirian, L., Novikov, A., Dickson, B., Bühler, K., & Langs, G. (2014). Structure-based neuron retrieval across drosophila brains. Neuroinformatics, 12, 423–434.

    Article  PubMed  Google Scholar 

  9. Grubbs, F.E. (1950). Sample criteria for testing outlying observations. The Annals of Mathematical Statistics, 21, 27–58.

    Article  Google Scholar 

  10. Herberich, G., Windoffer, R., Leube, R., & Aach, T. (2011). 3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy. In Proceedings of Engineering in Medicine and Biology Society (EMBC).

  11. Herberich, G., Friedrich, A., Aach, T., Windoffer, R., & Leube, R.E. (2012). Flux-based 3D segmentation of keratin intermediate filaments in confocal laser scanning microscopy. In Proceeding of IEEE International Symposium on Biomedical Imaging (ISBI).

  12. Jimenez-Carretero, D., Santos, A., Kerkstra, S., Rudyanto, R.D., & Ledesma-Carbayo, M.J. (2013). 3D frangi-based lung vessel enhancement filter penalizing airways. In Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI).

  13. Kapur, J., Sahoo, P., & Wong, A. (1985). A new method for gray-level picture thresholding using the entropy of the histogram. Computer Vision Graphics, and Image Processing, 29(3), 273–285.

    Article  Google Scholar 

  14. Lorenz, C., Carlsen, I.C., Buzug, T.M., Fassnacht, C., & Weese, J. (1997). Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images. In CVRMed-MRCAS: Springer.

  15. Masse, N.Y., Cachero, S., Ostrovsky, A.D., & Jefferis, G.S.X.E. (2012). A mutual information approach to automate identification of neuronal clusters in drosophila brain images. Frontiers in Neuroinformatics, 6, 21.

  16. Ming, X., Li, A., Wu, J., Yan, C., Ding, W., Gong, H., Zeng, S., & Liu, Q. (2013). Rapid reconstruction of 3D neuronal morphology from light microscopy images with augmented rayburst sampling. PLoS ONE, 8, e84,557.

  17. Olabarriaga, S., Breeuwer, M., & Niessen, W. (2003). Evaluation of hessian-based filters to enhance the axis of coronary arteries in CT images. International Congress Series, 1256, 1191–1196.

    Article  Google Scholar 

  18. Otsu, N. (1975). A threshold selection method from gray-level histograms. Automatica, 11, 23–27.

    Google Scholar 

  19. Prewitt, J.M.S., & Mendelsohn, M.L. (2006). The analysis of cell images*. Annals of the New York Academy of Sciences, 128(3), 1035–1053.

    Article  Google Scholar 

  20. Rohlfing, T., & Maurer, C. (2003). Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Transactions on Information Technology in Biomedicine, 7, 16–25.

    Article  PubMed  Google Scholar 

  21. Rutovitz, D. (1968). Data structures for operations on digital images. Pictorial pattern recognition, 105–133.

  22. Sato, Y., Nakajima, S., Atsumi, H., Koller, T., Gerig, G., Yoshida, S., & Kikinis, R. (1997). 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Lecture Notes in Computer Science pp 213–222.

  23. Schulze, F., Trapp, M., Bühler, K., Liu, T., & Dickson, B.J. (2012). Similarity based object retrieval of composite neuronal structures. In Proceedings of the 5th Eurographics conference on 3D Object Retrieval.

  24. Trapp, M., Schulze, F., Bühler, K., Liu, T, & Dickson, B.J. (2013). 3D object retrieval in an atlas of neuronal structures. The Visual Computer, 29(12), 1363–1373.

    Article  Google Scholar 

  25. Tsai, W.H. (1985). Moment-preserving thresholding: A new approach. Computer Vision Graphics, and Image Processing, 29(3), 377–393.

    Article  Google Scholar 

  26. Tsoumakas, G., Katakis, I., & Vlahavas, I. (2010). Data Mining and Knowledge Discovery Handbook. Springer US, chap Mining Multi-label Data, 667–685.

  27. Xiao, H., & Peng, H. (2013). App2: automatic tracing of 3d neuron morphology based on hierarchical pruning of a gray-weighted image distance-tree. Bioinformatics, 29(11), 1448–1454.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Yu, J.Y., Kanai, M.I., Demir, E., Jefferis, G.S., & Dickson, B.J. (2010). Cellular organization of the neural circuit that drives drosophila courtship behavior. Current Biology, 20, 1602–1614.

    CAS  Article  PubMed  Google Scholar 

  29. Zhou, Z., Sorensen, S., Zeng, H., Hawrylycz, M., & Peng, H. (2014). Adaptive image enhancement for tracing 3D morphologies of neurons and brain vasculatures. Neuroinformatics, 1–14.

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Acknowledgments

This work was funded by the FFG Headquarter Project “Molecular Basis” grant number 834223. We would like to thank Barry J. Dickson (Janelia Research Campus, USA) and Pawel Pasierbek (Research Institute of Molecular Pathology, Austria) for the image data and their advice. We would also like to thank Anne von Philipsborn (Aarhus University, Denmark) for providing the secondary dataset.

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Correspondence to Florian Schulze.

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Trapp, M., Schulze, F., Novikov, A.A. et al. Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images. Neuroinform 14, 221–233 (2016). https://doi.org/10.1007/s12021-015-9289-y

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Keywords

  • Adaptive image enhancement
  • Background-aware
  • Neuron
  • Confocal microscopy
  • Drosophila
  • GAL4 enhancement
  • BrainGazer