InSegtCone: interactive segmentation of crystalline cones in compound eyes

Understanding the diversity of eyes is crucial to unravel how different animals use vision to interact with their respective environments. To date, comparative studies of eye anatomy are scarce because they often involve time-consuming or inefficient methods. X-ray micro-tomography is a promising high-throughput imaging technique that enables to reconstruct the 3D anatomy of eyes, but powerful tools are needed to perform fast conversions of anatomical reconstructions into functional eye models. We developed a computing method named InSegtCone to automatically segment the crystalline cones in the apposition compound eyes of arthropods. Here, we describe the full auto-segmentation process, showcase its application to three different insect compound eyes and evaluate its performance. The auto-segmentation could successfully label the full individual shapes of 60%-80% of the crystalline cones, and is about as accurate and 250 times faster than manual labelling of the individual cones. We believe that InSegtCone can be an important tool for peer scientists to enable extensive comparisons of the diversity of eyes and vision in arthropods.