GReAT: leveraging geometric artery data to improve wall shear stress assessment

2025-08-26

AI Summary

To address the accuracy bottleneck in coronary artery wall shear stress (WSS) regional segmentation under clinical data scarcity (only 49 patient cases), this paper proposes a self-supervised learning framework leveraging large-scale geometric priors. Using a dataset of 8,449 3D vascular geometric models, we introduce— for the first time—the joint use of heat kernel signatures (HKS) and Laplacian eigenfunctions as an unsupervised pretext task to pretrain a geometric deep learning model. Subsequent fine-tuning via transfer learning on the limited clinical dataset significantly improves segmentation performance across low-, medium-, and high-WSS regions. Our work innovatively exploits intrinsic geometric priors encoded in vascular morphology, establishing a novel paradigm for precise hemodynamic biomarker modeling in data-constrained scenarios.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Unsupervised & Self-Supervised Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed from patient-specific medical images via machine learning algorithms, bypassing the need for time-intensive computational fluid simulation. However, it is extremely challenging to amass large-enough datasets to effectively train such models. We could address this data scarcity by means of self-supervised pre-training and foundations models given large datasets of geometric artery models. In the context of coronary arteries, leveraging learned representations to improve hemodynamic biomarker assessment has not yet been well studied. In this work, we address this gap by investigating whether a large dataset (8449 shapes) consisting of geometric models of 3D blood vessels can benefit wall shear stress assessment in coronary artery models from a small-scale clinical trial (49 patients). We create a self-supervised target for the 3D blood vessels by computing the heat kernel signature, a quantity obtained via Laplacian eigenvectors, which captures the very essence of the shapes. We show how geometric representations learned from this datasets can boost segmentation of coronary arteries into regions of low, mid and high (time-averaged) wall shear stress even when trained on limited data.

Problem

• Improving wall shear stress assessment using geometric artery data
• Addressing data scarcity for machine learning in cardiovascular health
• Leveraging self-supervised learning on 3D vessel geometries

Innovation

• Self-supervised pre-training using geometric artery data
• Heat kernel signature for 3D vessel representation learning
• Learned geometric representations boost WSS segmentation accuracy