Surf_2_Volume: A Python Package for Converting CIFTI Cortical Parcellations to NIfTI Volumes
Abstract
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.
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BibTeX
@misc{yang2026surf2volume,
title = {{Surf_2_Volume: A Python Package for Converting CIFTI Cortical Parcellations to NIfTI Volumes}},
author = {Shuguang Yang and Ziyi Wang and Yujing Shen and Zhengye Wang and Junyi Li and Yujing Nie and Feizhen Cao and Suiping Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2608.27012},
eprint = {2608.27012},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2608.27012},
}
RIS
TY - UNPB
TI - Surf_2_Volume: A Python Package for Converting CIFTI Cortical Parcellations to NIfTI Volumes
AU - Shuguang Yang
AU - Ziyi Wang
AU - Yujing Shen
AU - Zhengye Wang
AU - Junyi Li
AU - Yujing Nie
AU - Feizhen Cao
AU - Suiping Wang
PY - 2026
DA - 2026/10/04
PB - arXiv
DO - 10.48550/arxiv.2608.27012
UR - https://arxiv.org/abs/2608.27012
AB - 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.
ER -