发表机构
École de technologie supérieure（高等工程技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SimCortex v2提出联合重建左右白质与软膜表面的深度学习框架，利用带状条件U-Net预测多尺度速度场，在560例数据上实现近零碰撞与最低自交率，同时保持高精度。
AI 中文摘要
从结构磁共振成像（MRI）重建皮层白质（WM）和软膜表面是基于表面的神经解剖学分析的前提，但由于皮层薄且紧密折叠，这一任务仍然具有挑战性。重建方法可能产生几何伪影，如网格自交和皮层表面之间的碰撞，尽管最近的深度学习方法已将重建时间从数小时缩短至数分钟，但这些伪影仍然存在。我们提出SimCortex v2，一个用于从T1加权MRI同时重建左右WM和软膜表面的深度学习框架。SimCortex v2从体积分割中估计拓扑正确的初始表面，并使用由带状条件、类U-Net网络预测的多尺度平稳速度场联合细化所有四个表面。我们在来自14个队列的560例病例上评估了SimCortex v2，其中13个队列在训练中未见，年龄范围6-89岁，涵盖健康与临床人群，以及来自三个供应商的扫描仪。SimCortex v2匹配了最强基线的表面距离精度（平均对称表面距离0.253毫米），同时在92.14%的病例中未检测到表面间碰撞，并且在基于学习的方法中具有最低的自交比例（0.044%），而每个基线在每个病例中都至少产生一次碰撞。源代码、配置文件、预训练权重、预处理数据以及精确评估划分已公开发布。
英文摘要
Reconstructing cortical WM and pial surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based neuroanatomical analysis, yet remains challenging because the cortex is thin and tightly folded. Reconstruction methods can produce geometric artifacts such as mesh self-intersections and collisions between cortical surfaces, and although recent deep learning methods have reduced reconstruction time from hours to minutes, these artifacts persist. We propose SimCortex v2, a deep learning framework for simultaneous reconstruction of the left and right WM and pial surfaces from T1-weighted MRI. SimCortex v2 estimates topologically correct initial surfaces from a volumetric segmentation and refines all four jointly using multi-scale stationary velocity fields predicted by a ribbon-conditioned, U-Net-like network. We evaluated SimCortex v2 on 560 cases from 14 cohorts, thirteen of them unseen during training, spanning ages 6-89, healthy and clinical populations, and scanners from three vendors. SimCortex v2 matched the surface-distance accuracy of the strongest baseline (average symmetric surface distance 0.253 mm) while showing no detected inter-surface collision in 92.14% of cases and the lowest self-intersection fraction (0.044%) among learning-based methods, whereas every baseline produced at least one collision in every case. Source code, configuration files, pretrained weights, preprocessed data, and the exact evaluation splits are publicly released.
CommentsSubmitted to Medical Image Analysis
