  Existing learning-based methods to automatically trace axons in 3D brain
imagery often rely on manually annotated segmentation labels. Labeling is a
labor-intensive process and is not scalable to whole-brain analysis, which is
needed for improved understanding of brain function. We propose a
self-supervised auxiliary task that utilizes the tube-like structure of axons
to build a feature extractor from unlabeled data. The proposed auxiliary task
constrains a 3D convolutional neural network (CNN) to predict the order of
permuted slices in an input 3D volume. By solving this task, the 3D CNN is able
to learn features without ground-truth labels that are useful for downstream
segmentation with the 3D U-Net model. To the best of our knowledge, our model
is the first to perform automated segmentation of axons imaged at subcellular
resolution with the SHIELD technique. We demonstrate improved segmentation
performance over the 3D U-Net model on both the SHIELD PVGPe dataset and the
BigNeuron Project, single neuron Janelia dataset.
