ArticleBMC bioinformatics2017
Semi-automated quantitative Drosophila wings measurements.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- Conserved wing shape variation across biological scales unveils dialectical relationships between micro- and macroevolution.Communications biology · 2025Article
- Using comprehensive machine-learning models to classify complex morphological characters.Ecology and evolution · 2021Article
- Correlative microscopy and block-face imaging (CoMBI) method for both paraffin-embedded and frozen specimens.Scientific reports · 2021Article
- Dataset of wing venation measurements forBiodiversity data journal · 2020Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrosophila melanogaster is an important organism used in many fields of biological research such as genetics and developmental biology. Drosophila wings have been widely used to study the genetics of development, morphometrics and evolution. Therefore there is much interest in quantifying wing structures of Drosophila. Advancement in technology has increased the ease in which images of Drosophila can be acquired. However such studies have been limited by the slow and tedious process of acquiring phenotypic data.
resultsWe have developed a system that automatically detects and measures key points and vein segments on a Drosophila wing. Key points are detected by performing image transformations and template matching on Drosophila wing images while vein segments are detected using an Active Contour algorithm. The accuracy of our key point detection was compared against key point annotations of users. We also performed key point detection using different training data sets of Drosophila wing images. We compared our software with an existing automated image analysis system for Drosophila wings and showed that our system performs better than the state of the art. Vein segments were manually measured and compared against the measurements obtained from our system.
conclusionOur system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.
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Registered trials
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