Synthesis of Ground Truth for Neuronal Segmentation

Segmenting neuropil data for connectomics is limited by the high cost and effort of manually labeling Electron Microscopy (EM) volumes. To address this, we propose a two-stage framework to generate large amounts of realistic image data with ground-truth segmentations. First, we use Procedural Content Generation (PCG) with a parametric evolution strategy to synthesize diverse 3D neuron morphologies, which are combined into dense, noise-free segmentation masks. Second, these masks guide a conditional denoising diffusion model (DDPM) to generate realistic EM image patches, trained adversarially with a PatchGAN discriminator. This approach enables scalable production of high-quality training data, aiming to improve model accuracy and reduce manual annotation efforts.