用级联扩散模型生成逼真细胞图像，提升分割精度
Cascaded Diffusion Models for 2D and 3D Microscopy Image Synthesis to Enhance Cell Segmentation
- 分层扩散模型从稀疏标注生成2D/3D细胞掩码
- 合成数据+真实数据训练使分割性能提升9%
- 适合缺乏标注数据的生物图像研究者
自动化细胞分割对生物医学研究至关重要，但传统方法依赖人工且易出错。深度学习虽有效，却需大量标注数据，而手动标注成本高、数据稀缺。为此，我们提出一种新型框架，利用级联扩散模型合成密集标注的2D与3D细胞显微图像。该方法通过多层级扩散模型与NeuS（一种3D表面重建方法）从稀疏2D标注生成2D和3D细胞掩码；随后微调预训练的2D Stable Diffusion模型生成真实细胞纹理，并融合生成细胞群体。实验表明，将合成数据与真实数据联合训练后，多个数据集上的细胞分割性能最高提升9%。同时，FID分数显示合成数据与真实数据高度相似。代码将公开于https://github.com/ruveydayilmaz0/cascaded_diffusion。
原文摘要 · Abstract (English)
Automated cell segmentation in microscopy images is essential for biomedical research, yet conventional methods are labor-intensive and prone to error. While deep learning-based approaches have proven effective, they often require large annotated datasets, which are scarce due to the challenges of manual annotation. To overcome this, we propose a novel framework for synthesizing densely annotated 2D and 3D cell microscopy images using cascaded diffusion models. Our method synthesizes 2D and 3D cell masks from sparse 2D annotations using multi-level diffusion models and NeuS, a 3D surface reconstruction approach. Following that, a pretrained 2D Stable Diffusion model is finetuned to generate realistic cell textures and the final outputs are combined to form cell populations. We show that training a segmentation model with a combination of our synthetic data and real data improves cell segmentation performance by up to 9\% across multiple datasets. Additionally, the FID scores indicate that the synthetic data closely resembles real data. The code for our proposed approach will be available at https://github.com/ruveydayilmaz0/cascaded_diffusion.
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