Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for
  Cortical Surface Reconstruction
Shanlin Sun Thanh-Tung Le Chenyu You Hao Tang Kun Han Haoyu Ma Deying Kong Xiangyi Yan Xiaohui Xie
We present Hybrid-CSR, a geometric deep-learning model that combines explicit
and implicit shape representations for cortical surface reconstruction.
Specifically, Hybrid-CSR begins with explicit deformations of template meshes
to obtain coarsely reconstructed cortical surfaces, based on which the oriented
point clouds are estimated for the subsequent differentiable poisson surface
reconstruction. By doing so, our method unifies explicit (oriented point
clouds) and implicit (indicator function) cortical surface reconstruction.
Compared to explicit representation-based methods, our hybrid approach is more
friendly to capture detailed structures, and when compared with implicit
representation-based methods, our method can be topology aware because of
end-to-end training with a mesh-based deformation module. In order to address
topology defects, we propose a new topology correction pipeline that relies on
optimization-based diffeomorphic surface registration. Experimental results on
three brain datasets show that our approach surpasses existing implicit and
explicit cortical surface reconstruction methods in numeric metrics in terms of
accuracy, regularity, and consistency.