Abstract
Drosophila melanogaster is a well-known model organism that can be used for studying oogenesis (egg chamber development) including gene expression patterns. Standard analysis methods require manual segmentation of individual egg chambers, which is a difficult and time-consuming task. We present an image processing pipeline to detect and localize Drosophila egg chambers that consists of the following steps: (i) superpixel-based image segmentation into relevant tissue classes; (ii) detection of egg center candidates using label histograms and ray features; (iii) clustering of center candidates and; (iv) area-based maximum likelihood ellipse model fitting. Our proposal is able to detect 96% of human-expert annotated egg chambers at relevant developmental stages with less than 1% false-positive rate, which is adequate for the further analysis.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bastock, R., St. Johnston, D.: Drosophila oogenesis. Curr. Biol. 18, R1082–R1087 (2008)
Parton, R.M., Vallés, A.M., Dobbie, I.M., Davis, I.: Isolation of Drosophila egg chambers for imaging. Cold Spring Harb. Protoc. (2010). doi:10.1101/pdb.prot5402
Jia, D., Xu, Q., Xie, Q., Mio, W., Deng, W.M.: Automatic stage identification of Drosophila egg chamber based on DAPI images. Sci. Rep. 6, 18850 (2016)
Borovec, J., Kybic, J.: Binary pattern dictionary learning for gene expression representation in Drosophila imaginal discs. In: Chen, C.-S., Lu, J., Ma, K.-K. (eds.) ACCV 2016. LNCS, vol. 10117, pp. 555–569. Springer, Cham (2017). doi:10.1007/978-3-319-54427-4_40
Tomancak, P., et al.: Global analysis of patterns of gene expression during Drosophila embryogenesis. Genome Biol. 8, R145 (2007)
Jug, F., Pietzsch, T., Preibisch, S., Tomancak, P.: Bioimage informatics in the context of Drosophila research. Methods 68, 60–73 (2014)
Castro, C., Luengo-Oroz, M., Douloquin, L., et al.: Image processing challenges in the creation of spatiotemporal gene expression atlases of developing embryos. IEEE Eng. Med. Biol. Soc. (EMBC) 2011, 6841–6844 (2011). https://www.ncbi.nlm.nih.gov/pubmed/22255910
Nava, R., Kybic, J.: Supertexton-based segmentation in early Drosophila oogenesis. In: IEEE International Conference on Image Processing (ICIP), pp. 2656–2659 (2015)
Achanta, R., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE PAMI 34, 2274–2282 (2012)
Boykov, Y., Veksler, O.: Fast approximate energy minimization via graph cuts. IEEE Pattern Anal. Mach. Intell. 23, 1222–1239 (2001)
Smith, K., Carleton, A., Lepetit, V.: Fast ray features for learning irregular shapes. In: IEEE 12th International Conference on Computer Vision, pp. 397–404 (2009)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Halir, R., Flusser, J.: Numerically stable direct least squares fitting of ellipses. Cent. Eur. Comput. Graph. Visual. 98, 125–132 (1998)
Chen, Q., Yang, X., Petriu, E.: Watershed segmentation for binary images with different distance transforms. IEEE HAVE 2, 111–116 (2004)
Acknowledgements
This work was supported by Czech Science Foundation projects no. 14-21421S and 17-15361S, and Mexican agency CONACYT with the postdoctoral scholarship no. 266758. The images were provided by Pavel Tomancak’s group, MPI-CBG, Germany.
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
Borovec, J., Kybic, J., Nava, R. (2017). Detection and Localization of Drosophila Egg Chambers in Microscopy Images. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)