SimCortex: Collision-free Simultaneous Cortical Surfaces Reconstruction
This addresses the need for reliable neuroanatomical analyses in neuroscience by providing a more accurate and topologically sound method for cortical surface reconstruction, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of cortical surface reconstruction from MRI data, which often suffers from overlaps and topological defects, by introducing SimCortex, a deep learning framework that simultaneously reconstructs all brain surfaces while preserving topology, resulting in dramatically reduced surface collisions and self-intersections while maintaining state-of-the-art geometric accuracy.
Accurate cortical surface reconstruction from magnetic resonance imaging (MRI) data is crucial for reliable neuroanatomical analyses. Current methods have to contend with complex cortical geometries, strict topological requirements, and often produce surfaces with overlaps, self-intersections, and topological defects. To overcome these shortcomings, we introduce SimCortex, a deep learning framework that simultaneously reconstructs all brain surfaces (left/right white-matter and pial) from T1-weighted(T1w) MRI volumes while preserving topological properties. Our method first segments the T1w image into a nine-class tissue label map. From these segmentations, we generate subject-specific, collision-free initial surface meshes. These surfaces serve as precise initializations for subsequent multiscale diffeomorphic deformations. Employing stationary velocity fields (SVFs) integrated via scaling-and-squaring, our approach ensures smooth, topology-preserving transformations with significantly reduced surface collisions and self-intersections. Evaluations on standard datasets demonstrate that SimCortex dramatically reduces surface overlaps and self-intersections, surpassing current methods while maintaining state-of-the-art geometric accuracy.