Tamara M. Mueller

Sophie Starck, Yadunandan Vivekanand Kini, Jessica Johanna Maria Ritter et al.

Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between chronological and biological age. To improve understanding of age-related changes in various body parts, we investigate the ageing of the human body on a large scale by using whole-body 3D images. We utilise the Grad-CAM method to determine the body areas most predictive of a person's age. In order to expand our analysis beyond individual subjects, we employ registration techniques to generate population-wide importance maps that show the most predictive areas in the body for a whole cohort of subjects. We show that the investigation of the full 3D volume of the whole body and the population-wide analysis can give important insights into which body parts play the most important roles in predicting a person's age. Our findings reveal three primary areas of interest: the spine, the autochthonous back muscles, and the cardiac region, which exhibits the highest importance. Finally, we investigate differences between subjects that show accelerated and decelerated ageing.

Sophie Starck, Vasiliki Sideri-Lampretsa, Bernhard Kainz et al.

Anatomical atlases are widely used for population studies and analysis. Conditional atlases target a specific sub-population defined via certain conditions, such as demographics or pathologies, and allow for the investigation of fine-grained anatomical differences like morphological changes associated with ageing or disease. Existing approaches use either registration-based methods that are often unable to handle large anatomical variations or generative adversarial models, which are challenging to train since they can suffer from training instabilities. Instead of generating atlases directly in as intensities, we propose using latent diffusion models to generate deformation fields, which transform a general population atlas into one representing a specific sub-population. Our approach ensures structural integrity, enhances interpretability and avoids hallucinations that may arise during direct image synthesis by generating this deformation field and regularising it using a neighbourhood of images. We compare our method to several state-of-the-art atlas generation methods using brain MR images from the UK Biobank. Our method generates highly realistic atlases with smooth transformations and high anatomical fidelity, outperforming existing baselines. We demonstrate the quality of these atlases through comprehensive evaluations, including quantitative metrics for anatomical accuracy, perceptual similarity, and qualitative analyses displaying the consistency and realism of the generated atlases.