We present Biomedisa, a free and easy-to-use open-source online platform developed for semi-automatic segmentation of large volumetric images. The segmentation is based on a smart interpolation of sparsely pre-segmented slices taking into account the complete underlying image data. Biomedisa is particularly valuable when little a priori knowledge is available, e.g. for the dense annotation of the training data for a deep neural network. The platform is accessible through a web browser and requires no complex and tedious configuration of software and model parameters, thus addressing the needs of scientists without substantial computational expertise. We demonstrate that Biomedisa can drastically reduce both the time and human effort required to segment large images. It achieves a significant improvement over the conventional approach of densely pre-segmented slices with subsequent morphological interpolation as well as compared to segmentation tools that also consider the underlying image data. Biomedisa can be used for different 3D imaging modalities and various biomedical applications.

No abstract

Analysing large numbers of brain samples is challenging but can reveal minor but statistically and biologically relevant variations, that provide important insights into animal behaviour, ecology and evolution. Here, we used micro-CT imaging and deep learning to perform automatic analyses of 187 bee brains (honey bees and bumblebees). This large-scale quantitative comparative analysis of 3D brain data revealed strong inter-individual variations in overall bee brain size that is consistent across colonies and species, suggesting selection processes supporting behavioural variability. In addition, analyses of bumblebee brains revealed a significant level of lateralization in the optic lobes, likely related to reported variations in visual learning. Our fast and accurate deep learning-based approach to process bee brain data is user friendly and holds considerable promises for conducting large-scale quantitative neuroanatomical comparisons across a wide range of species beyond insects. Ultimately, this will help address fundamental unsolved questions related to the evolution of animal brains and cognition.