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

翻译：本研究提出一种利用动态4D-CTA头部扫描标注脑血管系统的新方法。通过提取动态CTA采集中的多个时间点数据，我们通过减除骨骼与软组织以增强动静脉显影，从而显著降低人工标注脑血管的工作量。基于此标注数据，我们利用动态4D-CTA序列中多时相共享分割标签的特性训练深度学习模型，使数据集规模有效扩大4至5倍，并增强了对造影剂时相的鲁棒性。我们的数据集共包含25例患者的110幅训练图像及14例患者的165幅测试图像。与另两个规模相近的CTA脑血管分割数据集相比，基于本数据集训练的nnUNet模型在所有血管区域均取得显著更优的分割效果：在TopBrain数据集中，动脉平均mDC达0.846，静脉达0.957。此外，平均定向豪斯多夫距离（adHD）与拓扑敏感度（tSens）指标呈现相同趋势：使用本数据集可获得较低误差边界（动脉adHD为0.304毫米，静脉为0.078）与较高敏感度（动脉tSens为0.877，静脉为0.974），表明其在血管形态捕捉方面具有优异精度。我们的代码与模型权重已发布于https://github.com/alceballosa/robust-vessel-segmentation。