Abstract
While total intracranial carotid artery calcification (ICAC) volume is an established stroke biomarker, growing evidence shows this aggregate metric ignores the critical influence of plaque location, since calcification in different segments carries distinct prognostic and procedural risks. However, a finer-grained, segment-specific quantification has remained technically infeasible. Conventional 3D models are forced to process downsampled volumes or isolated patches, sacrificing the global context required to resolve anatomical ambiguity and render reliable landmark localization. To overcome this, we reformulate the 3D challenge as a \textbf{Parallel Probabilistic Landmark Localization} task along the 1D axial dimension. We propose the \textbf{Depth-Sequence Transformer (DST)}, a framework that processes full-resolution CT volumes as sequences of 2D slices, learning to predict $N=6$ independent probability distributions that pinpoint key anatomical landmarks. Our DST framework demonstrates exceptional accuracy and robustness. Evaluated on a 100-patient clinical cohort with rigorous 5-fold cross-validation, it achieves a Mean Absolute Error (MAE) of \textbf{0.1 slices}, with \textbf{96\%} of predictions falling within a $\pm1$ slice tolerance. Furthermore, to validate its architectural power, the DST backbone establishes the best result on the public Clean-CC-CCII classification benchmark under an end-to-end evaluation protocol. Our work delivers the first practical tool for automated segment-specific ICAC analysis. The proposed framework provides a foundation for further studies on the role of location-specific biomarkers in diagnosis, prognosis, and procedural planning. Our code will be made publicly available.
Abstract (translated)
虽然全颅内颈动脉钙化（ICAC）总体积已被确立为中风的生物标志物，但越来越多的证据表明，这种聚合指标忽视了斑块位置的关键影响，因为不同节段中的钙化具有不同的预后和操作风险。然而，更精细、按节段进行的具体量化在技术上仍然是不可行的。传统的3D模型被迫处理降采样的体积或孤立的补丁，牺牲了解析解剖模糊性和可靠地标定位所需的整体上下文。为了解决这个问题，我们将3D挑战重新定义为沿1D轴向维度的**并行概率标定点定位**任务。我们提出了**深度序列变换器（DST）框架**，该框架将全分辨率CT体积作为2D切片序列处理，并学习预测6个独立的概率分布来确定关键解剖标志点的位置。 我们的DST框架表现出卓越的准确性和鲁棒性。在包含100名患者的临床队列上进行严格的五折交叉验证评估后，它实现了平均绝对误差（MAE）为**0.1片**的结果，并且有**96%**的预测落在±1片容差范围内。此外，为了验证其架构能力，DST骨干在公共Clean-CC-CCII分类基准上以端到端评估协议下取得了最佳结果。 我们的工作提供了首个用于自动节段特异性ICAC分析的实际工具。所提出的框架为后续研究奠定了基础，这些研究旨在探讨位置特定生物标志物在诊断、预后和程序规划中的作用。我们将公开发布相关代码。
URL
https://arxiv.org/abs/2507.08214
