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:传播模型是一种特殊的基因模型,能够从所学分布中合成新数据。我们引入了DISPR,这是一个基于扩散的模型,用二维(2D)单细胞显微镜图像解决三维(3D)细胞形状预测的反问题。使用2D显微镜图像,DISPR有条件地预测现实的3D形状重建。为了展示DISPR作为基于地貌的单一细胞分类任务中数据增强工具的适用性,我们从红血细胞组中提取形态特征,分为六类,高度不平衡。从DISPR预测中添加三个小类的特征,使宏观F1评分从$1<unk> text{macro}=55.2\pm 4.6\pm=$F1<unk> text{macro}=72.2\pm 4.9<unk> $。因此,我们证明扩散模型可以成功地应用于反生物医学问题,并学习从2D显微镜图像中以现实的形态特征重建3D形状。</s>