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.
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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.
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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
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DOI: https://doi.org/10.1007/978-3-319-67389-9_3
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