A model-based high throughput method for fecundity estimation in fruit fly studies. Issue 3 (2nd October 2018)

• Record Type: Journal Article
• Authors: Ng'oma, Enoch; King, Elizabeth G.; Middleton, Kevin M.
• Is Part Of: Fly. Volume 12:Issue 3/4(2018)
• Page Start: 183
• Page End: 190
• Publication Date: 2018-10-02
• Subjects: Image analysis -- fecundity -- Drosophila melanogaster -- quantitative biological imaging -- cross-validation; Drosophila -- Periodicals; 595.77
• DOI: 10.1080/19336934.2018.1562267
• Languages: English
• ISSNs: 1933-6934

Abstract

ABSTRACT: The ability to quantify fecundity is critically important to a wide range of experimental applications, particularly in widely-used model organisms such as Drosophila melanogaster . However, the standard method of manually counting eggs is time consuming and limits the feasibility of large-scale experiments. We develop a predictive model to automate the counting of eggs from images of eggs removed from the media surface and washed onto dark filter paper. Our method uses the simple relationship between the white area in an image and the number of eggs present to create a predictive model that performs well even at high egg densities where clumping can complicate the individual identification of eggs. A cross-validation approach demonstrates our method performs well, with a correlation between predicted and manually counted values of 0.88. We show how this method can be applied to a large data set where egg densities vary widely.