CTseg: A Tool for Brain CT Segmentation, Spatial Normalisation, and Volumetrics

TL;DR

CTseg is a new, freely available software tool that improves brain CT segmentation, spatial normalisation, and volumetrics, validated against MRI standards and outperforming existing methods.

Contribution

This paper introduces CTseg, a CT-specific pipeline extending SPM's segmentation framework, with systematic validation and open-source availability.

Findings

CTseg significantly outperforms MRI-based SPM segmentation baseline.

CTseg shows strong agreement in brain volume estimates.

CTseg enables reliable downstream analysis like sex classification.

Abstract

This paper presents and validates CTseg, a freely available software for brain CT segmentation, spatial normalisation, and volumetrics. CTseg builds on the Multi-Brain generative modelling framework, providing a CT-specific pipeline that produces tissue maps, deformation fields, and brain volume estimates in the same format as SPM's unified segmentation, thereby extending SPM's established analysis chain from MRI to CT. CTseg is designed for routine hospital CT scans without requiring preprocessing or resampling in deployment. Although CTseg has been adopted in clinical research spanning, among other things, stroke, dementia, and brain morphometry, a systematic validation against an independent reference standard has been lacking. Using paired MR/CT head scans, we evaluate CTseg across four dimensions: segmentation accuracy against an MRI-derived silver standard; spatial normalisation consistency through group-average sharpness and voxelwise coefficient of variation; brain volume agreement via intraclass correlation and Bland-Altman analysis; and downstream sex classification performance from normalised tissue maps. As a baseline, we apply SPM's MRI-based unified segmentation directly to the CT images. CTseg significantly outperformed this baseline for segmentation and normalisation, showed stronger TBV agreement, and achieved comparable TIV agreement. CTseg is freely available at https://github.com/WCHN/CTseg, and all experiment code is included in the repository for full reproducibility.