Unpaired Image Enhancement for Neurite Segmentation In X-Ray tomography

As the field of connectomics strives to tackle questions regarding increasingly large neuronal circuits, technologies improving imaging throughput will be vital. X-Ray Holographic Nanotomography (XNH) may play a key role, by allowing for fast, non-destructive, multi-resolution imaging. XNH is well suited for rapidly imaging large tissue volumes, with throughput easily increased at the cost of resolution and image quality. We therefore set out to systematically examine the potential for cycle-consistent generative adversarial networks (CycleGANs) to facilitate high-quality segmentation from low-quality data. Additionally, we introduce the Split CycleGAN, a modification of the original formulation designed to prevent collaboration between generators that could result in hidden features in generated images. We find that our new formulation, as well as the original CycleGAN, both improve segmentation results over the naive case, allowing for an approximately 64-fold imaging speed up.