Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

TL;DR

Hybrid-CSR is a novel deep-learning model that integrates explicit and implicit shape representations for more accurate and topology-aware cortical surface reconstruction, outperforming existing methods on multiple datasets.

Contribution

The paper introduces Hybrid-CSR, combining explicit mesh deformations with implicit surface modeling, and proposes a topology correction pipeline for improved cortical surface reconstruction.

Findings

Outperforms existing methods in accuracy, regularity, and consistency.

Effectively captures detailed brain surface structures.

Ensures topology correctness through a novel correction pipeline.

Abstract

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.