SimCortex同时重建脑皮层表面，避免重叠与自交，提升精度。
SimCortex: Collision-free Simultaneous Cortical Surfaces Reconstruction
- 九类组织分割生成无碰撞初始网格，用于多尺度变形。
- 使用平稳速度场减少90%以上表面重叠和自交现象。
- 适合需要高精度皮层重建的神经影像研究者使用。
从磁共振成像（MRI）数据中准确重建皮层表面对于可靠的神经解剖分析至关重要。现有方法面临复杂的皮层几何、严格的拓扑要求，常产生表面重叠、自相交和拓扑缺陷。为此，我们提出SimCortex，一种深度学习框架，可同时从T1加权（T1w）MRI体积中重建左右白质和外皮层表面，并保持拓扑性质。该方法首先将T1w图像分割为九类组织标签图，从中生成个体化的无碰撞初始表面网格。这些网格作为后续多尺度保角变形的精确初始化。通过基于缩放-平方法整合的平稳速度场（SVFs），实现平滑且拓扑保持的变换，显著减少表面碰撞与自交。在标准数据集上的评估表明，SimCortex大幅降低表面重叠与自交，优于现有方法，同时保持最先进的几何精度。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。