Abstract
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 calcification shape. We investigate three automated approaches for IAC subtype classification 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 their respective features and are evaluated using 5-fold cross-validation. The three methods achieved comparable performance. The best overall results were a weighted F1 (mean $\pm$ SD) of up to $ 72.4 \pm 2.0 $ for a single artery (sphericity approach) and $ 62.6 \pm 1.1$ for joint artery classification (automated visual approach). Performance was largely preserved when using automated rather than manual IAC segmentation masks, and we found the difference in weighted F1 to be non-significant for the sphericity and deep learning methods. 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
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SWITCH_006.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=tPiJfbfeWI
BibTex
@InProceedings{JinBen_Automated_MICCAISAT2026,
author = { Jin, Benjamin AND Valdés Hernández, Maria del C. AND Bortsov, Richard AND Wardlaw, Joanna M. AND Bos, Daniel AND Mair, Grant},
title = { { Automated Distinction of Intimal and Medial Intracranial Arterial Calcification from CT Head } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17255},
month = {pending},
page = {pending}
}