A Causality-Inspired Model for Intima-Media Thickening Assessment in Ultrasound Videos
Journal:
arXiv
Published Date:
Mar 16, 2025
Abstract
Carotid atherosclerosis represents a significant health risk, with its early
diagnosis primarily dependent on ultrasound-based assessments of carotid
intima-media thickening. However, during carotid ultrasound screening,
significant view variations cause style shifts, impairing content cues related
to thickening, such as lumen anatomy, which introduces spurious correlations
that hinder assessment. Therefore, we propose a novel causal-inspired method
for assessing carotid intima-media thickening in frame-wise ultrasound videos,
which focuses on two aspects: eliminating spurious correlations caused by style
and enhancing causal content correlations. Specifically, we introduce a novel
Spurious Correlation Elimination (SCE) module to remove non-causal style
effects by enforcing prediction invariance with style perturbations.
Simultaneously, we propose a Causal Equivalence Consolidation (CEC) module to
strengthen causal content correlation through adversarial optimization during
content randomization. Simultaneously, we design a Causal Transition
Augmentation (CTA) module to ensure smooth causal flow by integrating an
auxiliary pathway with text prompts and connecting it through contrastive
learning. The experimental results on our in-house carotid ultrasound video
dataset achieved an accuracy of 86.93\%, demonstrating the superior performance
of the proposed method. Code is available at
\href{https://github.com/xielaobanyy/causal-imt}{https://github.com/xielaobanyy/causal-imt}.
