CorticalFlow: A Diffeomorphic Mesh Deformation Module for Cortical Surface Reconstruction
This addresses the need for fast and anatomically plausible cortical surface reconstruction, which has been a major impediment to clinical relevance in neuroimaging.
The paper tackles brain cortical surface reconstruction by introducing CorticalFlow, a geometric deep-learning model that deforms a template mesh using diffeomorphic transformations, achieving superior surfaces and reducing computation time from 9.5 minutes to 1 second.
In this paper we introduce CorticalFlow, a new geometric deep-learning model that, given a 3-dimensional image, learns to deform a reference template towards a targeted object. To conserve the template mesh's topological properties, we train our model over a set of diffeomorphic transformations. This new implementation of a flow Ordinary Differential Equation (ODE) framework benefits from a small GPU memory footprint, allowing the generation of surfaces with several hundred thousand vertices. To reduce topological errors introduced by its discrete resolution, we derive numeric conditions which improve the manifoldness of the predicted triangle mesh. To exhibit the utility of CorticalFlow, we demonstrate its performance for the challenging task of brain cortical surface reconstruction. In contrast to current state-of-the-art, CorticalFlow produces superior surfaces while reducing the computation time from nine and a half minutes to one second. More significantly, CorticalFlow enforces the generation of anatomically plausible surfaces; the absence of which has been a major impediment restricting the clinical relevance of such surface reconstruction methods.