CathAction: A Benchmark for Endovascular Intervention Understanding

TL;DR

CathAction is a comprehensive large-scale dataset designed to advance endovascular intervention understanding through detailed annotations for catheterization actions, collision detection, and segmentation, enabling improved research and real-world application development.

Contribution

We introduce CathAction, a large-scale, annotated dataset for endovascular intervention analysis, addressing limitations of existing datasets and supporting diverse research tasks.

Findings

Benchmarking of recent methods on CathAction

Identification of key challenges in endovascular understanding

Open research questions highlighted for future work

Abstract

Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive annotations necessary for broader endovascular intervention understanding. To tackle these limitations, we introduce CathAction, a large-scale dataset for catheterization understanding. Our CathAction dataset encompasses approximately 500,000 annotated frames for catheterization action understanding and collision detection, and 25,000 ground truth masks for catheter and guidewire segmentation. For each task, we benchmark recent related works in the field. We further discuss the challenges of endovascular intentions compared to traditional computer vision tasks and point out open research questions. We hope that CathAction will facilitate the development of endovascular intervention understanding methods that can be applied to real-world applications. The dataset is available at https://airvlab.github.io/cathaction/.