UC Irvine

We introduce a novel method leveraging conditional generative adversarial networks (cGANs) to generate diverse, high-resolution microscopy images for neuron tracing model training. This approach addresses the challenge of limited annotated data availability, a significant obstacle in automating neuron dendrite tracing. Our technique utilizes a multi-scale cascade process to generate synthetic images from single neuron tractograms, accurately replicating the complex characteristics of real microscopy images, encompassing imaging artifacts and background structures. In experiments, our method generates diverse images that mimic the characteristics of two distinct neuron microscopy datasets, which were successfully used as training data in the segmentation task of real neuron images.