Semi-automated quantitative Drosophila wings measurements

• Published in: BMC bioinformatics Vol. 18; no. 1; p. 319
• Main Authors: Loh, Sheng Yang Michael, Ogawa, Yoshitaka, Kawana, Sara, Tamura, Koichiro, Lee, Hwee Kuan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 28.06.2017; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Animals; Annotations; Automated detection; Automation; Biological evolution; Biological research; Computer programs; Developmental biology; Drosophila; Drosophila - physiology; Drosophila melanogaster; Evolution; Female; Genetic aspects; Genetics; Genome-wide association studies; Genomes; Image acquisition; Image analysis; Image processing; Image Processing, Computer-Assisted; Image segmentation; Insects; Integrated software; Male; Morphology; Morphometry; Quantitative genetics; Researchers; Software; Studies; Template matching; Wing morphometrics; Wings; Wings, Animal - physiology; Automated detection; Image processing; Drosophila; Wing morphometrics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-017-1720-y

Drosophila 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. We 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. Our system was able to detect specific key points and vein segments from Drosophila wing images with high accuracy.