利用动态4D-CTA数据提升脑血管自动分割的准确性与鲁棒性
Robust automatic brain vessel segmentation in 3D CTA scans using dynamic 4D-CTA data
- 通过多时相减法增强血管可视化，减少人工标注负担
- 数据量扩展4-5倍，使模型在不同对比阶段均表现稳定
- 在多个指标上优于现有方法，适合临床高精度血管分析
本研究提出一种基于动态4D-CTA头颅扫描的脑血管标注新方法。利用动态CTA获取的多个时间点数据，通过减去骨骼和软组织，显著提升动脉与静脉的可视化效果，大幅降低人工标注工作量。随后，基于同一患者多相位数据训练深度学习模型，使训练集规模扩大4至5倍，增强模型对不同对比阶段的鲁棒性。总数据集包含25名患者的110张训练图像和14名患者的165张测试图像。相较于两个类似规模的CTA脑血管分割数据集，使用本数据集训练的nnUNet模型在所有血管区域均表现更优：在TopBrain数据集上，动脉平均mDC达0.846，静脉达0.957；平均定向豪斯多夫距离（adHD）分别为0.304 mm（动脉）和0.078 mm（静脉），拓扑敏感度（tSens）为0.877（动脉）和0.974（静脉），表明模型在形态捕捉上具有极高精度。代码与模型权重已公开于https://github.com/alceballosa/robust-vessel-segmentation。
原文摘要 · Abstract (English)
In this study, we develop a novel methodology for annotating the brain vasculature using dynamic 4D-CTA head scans. By using multiple time points from dynamic CTA acquisitions, we subtract bone and soft tissue to enhance the visualization of arteries and veins, reducing the effort required to obtain manual annotations of brain vessels. We then train deep learning models on our ground truth annotations by using the same segmentation for multiple phases from the dynamic 4D-CTA collection, effectively enlarging our dataset by 4 to 5 times and inducing robustness to contrast phases. In total, our dataset comprises 110 training images from 25 patients and 165 test images from 14 patients. In comparison with two similarly-sized datasets for CTA-based brain vessel segmentation, a nnUNet model trained on our dataset can achieve significantly better segmentations across all vascular regions, with an average mDC of 0.846 for arteries and 0.957 for veins in the TopBrain dataset. Furthermore, metrics such as average directed Hausdorff distance (adHD) and topology sensitivity (tSens) reflected similar trends: using our dataset resulted in low error margins (adHD of 0.304 mm for arteries and 0.078 for veins) and high sensitivity (tSens of 0.877 for arteries and 0.974 for veins), indicating excellent accuracy in capturing vessel morphology. Our code and model weights are available online at https://github.com/alceballosa/robust-vessel-segmentation
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