CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface ReconstructionDownload PDF

21 May 2021, 20:48 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: 3D Deep Learning, Geometric Deep Learning, Regular Surface Recosntruction, Cortical Surface Reconstruction
  • TL;DR: Geometric deep learning model that learns to diffeomorphic deform a regular template mesh towards a targeted object.
  • Abstract: 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 the 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.
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: https://lebrat.github.io/CorticalFlow/
12 Replies

Loading