Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the inverse problem of three-dimensional (3D) cell shape prediction from two-dimensional (2D) single cell microscopy images. Using the 2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D shape reconstructions. To showcase the applicability of DISPR as a data augmentation tool in a feature-based single cell classification task, we extract morphological features from the red blood cells grouped into six highly imbalanced classes. Adding features from the DISPR predictions to the three minority classes improved the macro F1 score from $F1_\text{macro} = 55.2 \pm 4.6\%$ to $F1_\text{macro} = 72.2 \pm 4.9\%$. We thus demonstrate that diffusion models can be successfully applied to inverse biomedical problems, and that they learn to reconstruct 3D shapes with realistic morphological features from 2D microscopy images.

翻译：扩散模型是一类特殊的生成模型，能够从学习到的分布中合成新数据。我们提出了DISPR，一种基于扩散的模型，用于解决从二维（2D）单细胞显微镜图像预测三维（3D）细胞形状的逆问题。以二维显微镜图像为先验，DISPR被条件化以预测逼真的三维形状重建。为了展示DISPR作为特征基单细胞分类任务中数据增强工具的适用性，我们从分为六个高度不平衡类别的红细胞中提取形态学特征。将来自DISPR预测的特征添加到三个少数类中，宏F1分数从$F1_\text{macro} = 55.2 \pm 4.6\%$提高到$F1_\text{macro} = 72.2 \pm 4.9\%$。因此，我们证明了扩散模型可以成功应用于生物医学逆问题，并且它们能够从二维显微镜图像中学习重建具有逼真形态特征的三维形状。