构建细粒度脑血管分割数据集，提升小血管识别能力
Scaling up fine-grained intracranial vessel annotations in computed tomography angiography
- 基于动态4D-CTA扫描生成动脉静脉精细分割图
- 引入通用动脉类使模型在各血管区域表现更优
- 适合医学图像分析与脑血管疾病研究者使用
本文提出SemanticVessel，一个用于计算机断层血管造影（CTA）中细粒度脑血管分割的数据集。利用动态4D-CTA扫描提供的高对比度，生成动脉与静脉的分割轨迹，并通过强度引导区域生长法完成大部分脑血管区域的初始分割，再由专业放射科医生标注20种独特动脉类别。不同于现有数据集将小动脉作为背景忽略，本工作将其合并为通用动脉类。由于4D-CTA多期采集特性，单期标签可复用于同系列其他期次，无需额外标注即可显著扩大数据规模。实验表明，训练时加入通用动脉类的模型在所有血管区域均实现更优的细粒度分割效果。代码、标注工具及模型权重将开源。
原文摘要 · Abstract (English)
In this work, we present SemanticVessel, a dataset for fine-grained brain vessel segmentation in computed tomography angiography scans. Based on the detailed contrast provided by dynamic 4D-CTA scans, we generate segmentation traces for arteries and veins. We then use intensity-guided region growing to obtain segmentations of the majority of vascular territories in the human brain, which are refined and annotated with 20 unique arterial classes by an expert radiologist. Unlike existing datasets, where minor arteries are discarded as background content, we merge these minor arteries into a generic arterial class. Due to the multiple-phase acquisition of dynamic 4D-CTA, labels for a single phase can be re-used for other phases in the same series, greatly increasing the size of our dataset with no additional annotation cost. The results show that models trained with the additional generic artery class produce better fine-grained segmentations across the board. We will make our code, annotation GUI, and model weights available to the scientific community. Code, weights, and data will be made available on https://github.com/alceballosa/robust-vessel-segmentation
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