Neuron segmentation in electron microscopy (EM) aims to reconstruct the complete neuronal connectome; however, current deep learning-based methods are limited by their reliance on large-scale training data and extensive, time-consuming manual annotations. Traditional methods augment the training set through geometric and photometric transformations; however, the generated samples remain highly correlated with the original images and lack structural diversity. To address this limitation, we propose a diffusion-based data augmentation framework capable of generating diverse and structurally plausible image-label pairs for neuron segmentation. Specifically, the framework employs a resolution-aware conditional diffusion model with multi-scale conditioning and EM resolution priors to enable voxel-level image synthesis from 3D masks. It further incorporates a biology-guided mask remodeling module that produces augmented masks with enhanced structural realism. Together, these components effectively enrich the training set and improve segmentation performance. On the AC3 and AC4 datasets under low-annotation regimes, our method improves the ARAND metric by 32.1% and 30.7%, respectively, when combined with two different post-processing methods. Our code is available at https://github.com/HeadLiuYun/NeuroDiff.

翻译：电子显微镜（EM）中的神经元分割旨在重建完整的神经元连接组；然而，当前基于深度学习的方法受限于其对大规模训练数据和耗时费力的人工标注的依赖。传统方法通过几何和光度变换来增强训练集；然而，生成的样本与原始图像高度相关，且缺乏结构多样性。为解决这一局限，我们提出了一种基于扩散的数据增强框架，能够为神经元分割生成多样且结构合理的图像-标签对。具体而言，该框架采用了一个具有多尺度条件化和EM分辨率先验的分辨率感知条件扩散模型，以实现从3D掩模进行体素级图像合成。它进一步结合了一个生物学引导的掩模重塑模块，该模块能生成具有增强结构真实性的增强掩模。这些组件共同有效地丰富了训练集并提升了分割性能。在低标注条件下的AC3和AC4数据集上，当结合两种不同的后处理方法时，我们的方法分别将ARAND指标提高了32.1%和30.7%。我们的代码可在 https://github.com/HeadLiuYun/NeuroDiff 获取。