Anatomical atlases are widely used for population analysis. Conditional atlases target a particular sub-population defined via certain conditions (e.g. demographics or pathologies) and allow for the investigation of fine-grained anatomical differences - such as morphological changes correlated with age. Existing approaches use either registration-based methods that are unable to handle large anatomical variations or generative models, which can suffer from training instabilities and hallucinations. To overcome these limitations, we use latent diffusion models to generate deformation fields, which transform a general population atlas into one representing a specific sub-population. By generating a deformation field and registering the conditional atlas to a neighbourhood of images, we ensure structural plausibility and avoid hallucinations, which can occur during direct image synthesis. We compare our method to several state-of-the-art atlas generation methods in experiments using 5000 brain as well as whole-body MR images from UK Biobank. Our method generates highly realistic atlases with smooth transformations and high anatomical fidelity, outperforming the baselines.

翻译：摘要：解剖学图谱广泛应用于群体分析。条件图谱针对由特定条件（如人口统计学特征或病理学特征）定义的特定亚群，能够研究精细的解剖学差异——例如与年龄相关的形态学变化。现有方法要么采用无法处理大解剖变异的配准方法，要么使用生成模型（可能面临训练不稳定和幻觉问题）。为克服这些局限性，我们利用潜在扩散模型生成形变场，将通用群体图谱转化为代表特定亚群的图谱。通过生成形变场并将条件图谱配准至图像邻域，我们确保结构合理性并避免直接图像合成中可能出现的幻觉。我们在英国生物样本库的5000张脑部及全身MR图像实验中，将我们的方法与多种先进图谱生成方法进行了比较。结果表明，我们的方法生成了具有平滑变换和高解剖保真度的极逼真图谱，性能优于基线方法。