Depth-Sequence Transformer (DST) for Segment-Specific ICA Calcification Mapping on Non-Contrast CT
Journal:
arXiv
Published Date:
Jul 10, 2025
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.
