发表机构
South China Normal University（华南师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Surf_2_Volume工作流结合多种工具将CIFTI脑区分割转换为NIfTI体素空间，在两项测试中其调整Dice分数均优于Connectome Workbench，可控制灰质覆盖范围，解决了现有转换工具的不足。
AI 中文摘要
以Connectivity Informatics Technology Initiative（CIFTI）格式分发的脑区分割结果无法直接用于许多需要体素输入的分析程序。现有转换选项可能会使皮层灰质中的体素未被标记，或将标签分配到灰质之外，具体取决于映射参数。我们提出Surf_2_Volume，这一工作流结合了Connectome Workbench、FreeSurfer、AFNI、neuromaps及Python图像处理工具，可将皮层和皮层下的CIFTI脑区分割转换为Neuroimaging Informatics Technology Initiative（NIfTI）体素。该工作流将皮层与皮层下成分分离，通过fsaverage及目标MNI152模板的表面表示传递皮层标签，使用可调整的灰质概率阈值限制体素分配，再将各成分重新组合。使用Cole-Anticevic全脑网络分区进行测试时，Surf_2_Volume的调整Dice分数为0.776，而所评估的Connectome Workbench设置的最高分数为0.637；在使用Schaefer 2018 17网络体素图谱的独立测试中，Surf_2_Volume的分数为0.727，Workbench最佳设置的分数为0.535。在两次图谱评估中，Surf_2_Volume的调整Dice分数均高于所评估的Workbench设置。该工作流为需NIfTI输入的软件提供了使用表面脑区分割的途径，同时可对灰质覆盖范围进行明确控制。
英文摘要
Many volume-based neuroimaging workflows require NIfTI label volumes, whereas a growing number of cortical parcellations are distributed in CIFTI surface space. We present Surf_2_Volume, a Python package for converting categorical CIFTI cortical parcellations into NIfTI volumes by resampling surface labels and filling a target cortical ribbon. The package provides configurable control over cortical-ribbon support and allows users either to specify the mapping threshold manually or to select suitable thresholds automatically according to the similarity between parcel-size distributions in the converted volume and the source CIFTI, without requiring a volumetric reference for threshold selection. We evaluated Surf_2_Volume using the Schaefer2018 100-parcel 7-network and 17-network atlases and compared its automatically selected outputs with multiple Connectome Workbench surface-to-volume mappings. Surf_2_Volume produced more continuous cortical coverage while avoiding the substantial underfilling observed with conservative Workbench mappings and the wider spatial expansion observed with more permissive nearest-vertex mappings. Across both atlases, the automatically selected Surf_2_Volume outputs showed consistently closer agreement with the paired reference volumes than the tested Workbench mappings. In HCP resting-state data, parcel time series extracted from Surf_2_Volume volumes also showed consistently high agreement with those obtained from the paired reference volumes. Together, these results support Surf_2_Volume as a configurable Python-based solution for converting CIFTI cortical parcellations to NIfTI volumes, with automatic threshold selection and stable performance across the two tested network parcellations.
