A Deep Learning Model for Accurate Segmentation of the Drosophila melanogaster Brain from Micro-CT Imaging

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, isotropic three-dimensional image information, and the ability to derive accurate quantitative data. 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-namely, the need for a large amount of training data, which is often limited in many Micro-CT studies. Here we show that accurate deep learning models can be trained using only 1-3 Micro-CT images of the adult Drosophila melanogaster brain using Dragonfly's pre-trained neural networks and minimal user knowledge. We further demonstrate the power of our model by showing that it can 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, facilitating a rapid assessment of novel phenotypes. Our models are freely available and can be refined based on individual user needs.

Micro-CT data can be automatically segmented and quantified using a deep learning model trained on as few as 3 samples, facilitating rapid comparison of developmental phenotypes.