Shape4D: Modelling the Spatiotemporal Deformation Patterns in 3D Shapes.

这项研究将开发新的数学方法和算法，使人口水平的纵向研究能够模拟三维生物物体的时空变形模式。使用新颖的几何和深度学习技术，它将创造新的方法，允许表征物体的3D形状如何随着衰老、疾病进展和与环境的相互作用而变形，并模拟解剖器官的时空变形。其益处包括更好地了解生长过程、退化性疾病进展的预测模型以及有助于设计适当缓解和干预战略的计算框架。

This research will develop new mathematical methods and algorithms that will enable the use of population-level longitudinal studies to model the spatial and temporal deformation patterns in 3D biological objects. Using novel geometric and deep learning techniques, it will create new methods that will allow the characterization of how the 3D shape of objects deforms with ageing, disease progression and interaction with their environment, and the simulation of spatiotemporal deformations in anatomical organs. Benefits include a better understanding of growth processes, predictive models of how degenerative diseases progress and a computational framework that will assist in designing proper mitigation and intervention strategies.