Robust Brain Extraction Tool for Nonenhanced CT and CT Angiography: CTA-BET

Purpose To develop and evaluate a deep learning-based brain extraction model, CTA-BET, capable of providing accurate brain segmentation for CT angiography (CTA) and non-contrast-enhanced CT (NCCT) images. Materials and Methods In this retrospective study, CTA-BET was trained using CTA data from multi-institutional cohorts (n = 100 patients) and validated on an external CTA dataset (n = 50 patients). NCCT validation was performed using the publicly available CQ500 dataset (n = 132 patients). The model's performance was compared with five benchmark noncommercial brain extraction tools. Dice score, Hausdorff distance, and z score-normalized histograms were used to evaluate segmentation performance. Results The CTA-BET model outperformed all benchmark models, achieving a mean Dice score of 0.99 (95% CI: 0.99, 0.99) on CTA data (P < .001 for all comparisons) and 0.98 (95% CI: 0.98, 0.99) on NCCT images (P < .001 for all comparisons). In terms of Hausdorff distance, CTA-BET demonstrated higher performance compared with other benchmark tools on CTA images (P < .001 for all comparisons). Conclusion CTA-BET outperformed benchmark brain extraction tools on both CTA and NCCT images, providing a robust and accurate solution that could enhance automated imaging analysis in clinical and research settings. Keywords: CT-Angiography, Head/Neck, Brain/Brain Stem, Computer Applications-3D, Comparative Studies, Experimental Investigations, Technology Assessment, Segmentation, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA 2025.