Automated segmentation of insect anatomy from micro-CT images using deep learning

Three-dimensional (3D) imaging, such as micro-computed tomography
(micro-CT), is increasingly being used by organismal biologists for
precise and comprehensive anatomical characterization. However, the
segmentation of anatomical structures remains a bottleneck in research,
often requiring tedious manual work. Here, we propose a pipeline for the
fully-automated segmentation of anatomical structures in micro-CT images
utilizing state-of-the-art deep learning methods, selecting the ant brain
as a test case. We implemented the U-Net architecture for 2D image
segmentation for our convolutional neural network (CNN), combined with
pixel-island detection. For training and validation of the network, we
assembled a dataset of semi-manually segmented brain images of 76 ant
species. The trained network predicted the brain area in ant images fast
and accurately; its performance tested on validation sets showed good
agreement between the prediction and the target, scoring 80% Intersection
over Union (IoU) and 90% Dice Coefficient (F1) accuracy. While manual
segmentation usually takes many hours for each brain, the trained network
takes only a few minutes. Furthermore, our network is generalizable for
segmenting the whole neural system in full-body scans, and works in tests
on distantly related and morphologically divergent insects (e.g., fruit
flies). The latter suggests that methods like the one presented here
generally apply across diverse taxa. Our method makes the construction of
segmented maps and the morphological quantification of different species
more efficient and scalable to large datasets, a step toward a big data
approach to organismal anatomy.