发表机构
University of Edinburgh; Erasmus MC; UK Dementia Research Institute; NHS Lothian（爱丁堡大学; 伊拉斯姆斯医学中心; 英国痴呆症研究所; 洛锡安国民保健署）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出三种自动化方法区分颅内动脉钙化的内膜与中膜亚型，基于形状嵌入的方法性能最佳，加权F1达71.5，证明全自动量化可行且稳健。
AI 中文摘要
颅内动脉钙化（IACs）是临床非对比增强头部CT扫描中的常见发现，与神经血管疾病相关。钙化可发生在动脉壁的内膜层或中膜层，这两种亚型在病因上有所不同，并可能具有不同的临床意义。放射科医生可根据钙化的形状在视觉上区分这些亚型。我们研究了三种从头部CT衍生的分割掩膜中对IAC进行亚型分类的自动化方法：（1）对既定放射学视觉评分的自动化改编，（2）基于球度的方法，以及（3）基于医学形状基础模型提取的形状嵌入的方法。所有方法在其计算的特征之上使用相同的轻量级分类流程，并通过5折交叉验证进行评估。这三种方法取得了相当的性能，其中基于嵌入的方法在整体结果上表现最佳，单动脉的加权F1（平均值±标准差）最高可达71.5±3.7，联合动脉分类的加权F1为59.8±1.7。当使用自动化而非手动IAC分割掩膜时，性能基本保持不变，我们发现加权F1的差异不显著。我们的结果表明，从头部CT进行全自动IAC亚型量化是可行的，并且对使用手动和自动化IAC分割掩膜保持稳健。代码见此https URL。
英文摘要
Intracranial arterial calcifications (IACs) are a common finding on clinical non-contrast enhanced head CT scans and are associated with neurovascular disease. Calcifications can occur in the intimal or medial layer of the arterial wall, subtypes that differ in aetiology and may have distinct clinical relevance. These subtypes can be visually distinguished by radiologists based on the shape of the calcifications. We investigate three automated approaches for subtype classification of IAC from head CT-derived segmentation masks: (1) an automated adaptation of the established radiological visual score, (2) a sphericity-based method, and (3) a method based on shape embeddings extracted by a medical shape foundation model. All approaches use the same lightweight classification pipeline on top of the features they compute and are evaluated using 5-fold cross-validation. The three methods achieved comparable performance, with the embedding-based approach yielding the best overall results with a weighted F1 (mean $\pm$ SD) of up to 71.5 $\pm$ 3.7 for a single artery and 59.8 $\pm$ 1.7 for the joint artery classification. Performance was largely preserved when using automated instead of manual IAC segmentation masks, and we found the difference in weighted F1 not significant. Our results show that fully automated IAC subtype quantification from head CT is feasible and remains robust to the use of manual and automated IAC segmentation masks. Code at https://github.com/bjin96/iac-subtyping.
CommentsAccepted at the Stroke and neurovascular diseases Workshop on Imaging and Treatment CHallenges @ MICCAI 2026
