Predicting a surface's intrinsic geometric properties (metric tensors) in continuous time, rather than directly predicting vertex positions or embeddings, produces more accurate and geometrically valid brain structure forecasts for clinical applications.
This paper presents MT-GNN, a graph neural network that predicts how brain structures will change over time by learning their intrinsic geometry (metric tensors) rather than directly predicting shape. Given a patient's past brain scans, the model forecasts the mathematical properties that define a surface's shape at future timepoints, then reconstructs the actual 3D surface.