Abstract
The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, and the advance of software platforms that enable accurate microstructure quantification. However, manual data analysis of Micro-CT images can be laborious and time intensive. Deep learning offers the ability to streamline this process but historically has included caveats, such as the need for a large amount of training data, which is often limited in many Micro-CT studies. Here we show that accurate 3D deep learning models can be trained using only 1-3 Micro-CT images of the adult Drosophila melanogaster brain using pre-trained neural networks and minimal user knowledge. We further demonstrate the power of our model by showing that it can be expanded to accurately segment the brain across different tissue contrast stains, scanner models, and genotypes. Finally, we show how the model can assist in identifying morphological similarities and differences between mutants based on volumetric quantification, enabling rapid assessment of novel phenotypes. Our models are freely available and can be adapted to individual users' needs.
