Semi-automated quantitative Drosophila wings measurements

BMC Bioinformatics

Software

Sara Kawana¹  Yoshitaka Ogawa¹  Koichiro Tamura²  Sheng Yang Michael Loh³  Hwee Kuan Lee⁴ 

[1] Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, 192-0397, Tokyo, Japan;Department of Biological Sciences, Tokyo Metropolitan University, Hachioji, 192-0397, Tokyo, Japan;Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, 192-0397, Tokyo, Japan;Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, 07-01, Matrix, Singapore, 138671, Singapore, Singapore;Imaging Informatics Division, Bioinformatics Institute, 30 Biopolis Street, 07-01, Matrix, Singapore, 138671, Singapore, Singapore;Research Center for Genomics and Bioinformatics, Tokyo Metropolitan University, Hachioji, 192-0397, Tokyo, Japan;

关键词: Drosophila;    Image processing;    Wing morphometrics;    Automated detection;   

DOI  :  10.1186/s12859-017-1720-y

 received in 2016-07-08, accepted in 2017-06-09,  发布年份 2017

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2017

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