An image database of Drosophila melanogaster wings for phenomic and biometric analysis

GigaScience

Ian Dworkin³  Sudarshan Chari¹  William R Pitchers¹  David VanderZee¹  Anne Sonnenschein² 

[1] Department of Integrative Biology, Michigan State University, East Lansing 48824, MI, USA;BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing 48824, MI, USA;Department of Biology, McMaster University, Hamilton, Ontario, L8S 4K1, Canada

关键词: Mutants;    Phenomics;    Computer vision;    Geometric morphometrics;    Drosophila;    Wing shape;   

Others  :  1206120
DOI  :  10.1186/s13742-015-0065-6

 received in 2014-12-31, accepted in 2015-05-04,  发布年份 2015

【 摘 要 】

Background

Extracting important descriptors and features from images of biological specimens is an ongoing challenge. Features are often defined using landmarks and semi-landmarks that are determined a priori based on criteria such as homology or some other measure of biological significance. An alternative, widely used strategy uses computational pattern recognition, in which features are acquired from the image de novo. Subsets of these features are then selected based on objective criteria. Computational pattern recognition has been extensively developed primarily for the classification of samples into groups, whereas landmark methods have been broadly applied to biological inference.

Results

To compare these approaches and to provide a general community resource, we have constructed an image database of Drosophila melanogaster wings - individually identifiable and organized by sex, genotype and replicate imaging system - for the development and testing of measurement and classification tools for biological images. We have used this database to evaluate the relative performance of current classification strategies. Several supervised parametric and nonparametric machine learning algorithms were used on principal components extracted from geometric morphometric shape data (landmarks and semi-landmarks). For comparison, we also classified phenotypes based on de novo features extracted from wing images using several computer vision and pattern recognition methods as implemented in the Bioimage Classification and Annotation Tool (BioCAT).

Conclusions

Because we were able to thoroughly evaluate these strategies using the publicly available Drosophila wing database, we believe that this resource will facilitate the development and testing of new tools for the measurement and classification of complex biological phenotypes.

【 授权许可】

   
2015 Sonnenschein et al.; licensee BioMed Central.

【 图 表 】

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