用扩散模型生成逼真神经元图像和标签，提升低标注数据下的分割效果。
Diffusion Model-Based Data Augmentation for Enhanced Neuron Segmentation
- 基于扩散模型生成多尺度、结构合理的3D神经元图像与标签对。
- 在低标注条件下，AC3和AC4数据集上ARAND指标分别提升32.1%和30.7%。
- 适合需要减少人工标注、提升小样本分割性能的研究者使用。
电子显微镜中的神经元分割旨在重建完整的神经连接组；然而，当前基于深度学习的方法受限于大规模训练数据及耗时的手动标注。传统数据增强通过几何与光度变换扩充数据集，但生成样本与原始图像相关性高，缺乏结构多样性。为此，我们提出一种基于扩散模型的数据增强框架，可生成多样且结构合理的图像-标签对。该框架采用具有多尺度条件与电镜分辨率先验的分辨率达标条件扩散模型，实现从3D掩码到体素级图像的合成，并引入生物学引导的掩码重构模块，提升增强掩码的结构真实性。两者结合显著丰富训练集，提升分割性能。在低标注设置下，该方法与两种后处理方法结合，在AC3和AC4数据集上分别使ARAND指标提升32.1%和30.7%。代码已开源：https://github.com/HeadLiuYun/NeuroDiff。
原文摘要 · Abstract (English)
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.
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