Surf_2_Volume: a workflow for converting CIFTI parcellations to NIfTI volume space

Abstract

Parcellations distributed in Connectivity Informatics Technology Initiative (CIFTI) format cannot be used directly in many analysis programs that require volume input. Existing conversion options may leave voxels in cortical gray matter unlabeled or assign labels outside gray matter, depending on the mapping parameters. We present Surf_2_Volume, a workflow that combines Connectome Workbench, FreeSurfer, AFNI, neuromaps, and Python image processing to convert cortical and subcortical CIFTI parcellations into Neuroimaging Informatics Technology Initiative (NIfTI) volumes. The workflow separates cortical and subcortical components, transfers cortical labels through fsaverage and a surface representation of the target MNI152 template, restricts voxel assignment using an adjustable probability threshold for gray matter, and recombines the components. Using the Cole-Anticevic Brain-wide Network Partition, Surf_2_Volume had an adjusted Dice score of 0.776, compared with a maximum of 0.637 among the evaluated Connectome Workbench settings. In a separate test using the Schaefer 2018 17-network volume atlas, the scores were 0.727 for Surf_2_Volume and 0.535 for the best Workbench setting. Across both atlas evaluations, Surf_2_Volume had higher adjusted Dice scores than the evaluated Workbench settings. The workflow provides a way to use surface parcellations in software that requires NIfTI input while allowing explicit control over gray matter coverage.