MAPHIS—Measuring arthropod phenotypes using hierarchical image segmentations

Abstract

Abstract Animal phenotypic traits are utilised in a variety of studies. Often the traits are measured from images. The processing of a large number of images can be challenging; nevertheless, image analytical applications, based on neural networks, can be an effective tool in automatic trait collection. Our aim was to develop a stand‐alone application to effectively segment an arthropod from an image and to recognise individual body parts: namely, head, thorax (or prosoma), abdomen and four pairs of appendages. It is based on convolutional neural network with U‐Net architecture trained on more than a thousand images showing dorsal views of arthropods (mainly of wingless insects and spiders). The segmentation model gave very good results, with the automatically generated segmentation masks usually requiring only slight manual adjustments. The application, named MAPHIS, can further (1) organise and preprocess the images; (2) adjust segmentation masks using a simple graphical editor; and (3) calculate various size, shape, colouration and pattern measures for each body part organised in a hierarchical manner. In addition, a special plug‐in function can align body profiles of selected individuals to match a median profile and enable comparison among groups. The usability of the application is shown in three practical examples. The application can be used in a variety of fields where measures of phenotypic diversity are required, such as taxonomy, ecology and evolution (e.g. mimetic similarity). Currently, the application is limited to arthropods, but it can be easily extended to other animal taxa.