Recent advances in imaging and high-performance computing have made it possible to image the entire human brain at the cellular level. This is the basis to study the multi-scale architecture of the brain regarding its subdivision into brain areas and nuclei, cortical layers, columns, and cell clusters down to single cell morphology Methods for brain mapping and cell segmentation exploit such images to enable rapid and automated analysis of cytoarchitecture and cell distribution in complete series of histological sections. However, the presence of inevitable processing artifacts in the image data caused by missing sections, tears in the tissue, or staining variations remains the primary reason for gaps in the resulting image data. To this end we aim to provide a model that can fill in missing information in a reliable way, following the true cell distribution at different scales. Inspired by the recent success in image generation, we propose a denoising diffusion probabilistic model (DDPM), trained on light-microscopic scans of cell-body stained sections. We extend this model with the RePaint method to impute missing or replace corrupted image data. We show that our trained DDPM is able to generate highly realistic image information for this purpose, generating plausible cell statistics and cytoarchitectonic patterns. We validate its outputs using two established downstream task models trained on the same data.

翻译：近年来，成像技术与高性能计算领域的进展使得在细胞水平上实现全人脑成像成为可能。这为研究大脑的多尺度结构奠定了基础，包括脑区与核团、皮层分层、柱状结构、细胞簇乃至单细胞形态的划分。脑图谱构建与细胞分割方法利用此类图像，能够对完整组织学切片序列中的细胞构筑与细胞分布实现快速自动化分析。然而，图像数据中因切片缺失、组织撕裂或染色差异导致的不可避免的处理伪影，仍是造成最终图像数据存在缺口的主要原因。为此，我们旨在提供一个能够可靠填补缺失信息、并遵循不同尺度下真实细胞分布的模型。受近期图像生成领域成功的启发，我们提出一种基于去噪扩散概率模型（DDPM）的方法，该模型使用经细胞体染色切片的透射光显微镜扫描图像进行训练。我们通过RePaint方法扩展该模型，用于填补缺失或替换受损图像数据。研究表明，经过训练的DDPM能够生成高度真实的图像信息，产生合理的细胞统计特征与细胞构筑模式。我们使用两个基于相同数据训练的成熟下游任务模型对其输出结果进行了验证。