# Fully Automatic and Real-Time Catheter Segmentation in X-Ray Fluoroscopy

arXiv: https://arxiv.org/abs/1707.05137 · HTML: https://mlsanity.com/paper/1707.05137/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2017.

## Built on by (most cited first, 13 of 13)

- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [CathAction: A Benchmark for Endovascular Intervention Understanding](https://mlsanity.com/paper/2408.13126/) (2024)
- [Shape-Sensitive Loss for Catheter and Guidewire Segmentation](https://mlsanity.com/paper/2311.11205/) (2023)
- [Real-time guidewire tracking and segmentation in intraoperative x-ray](https://mlsanity.com/paper/2404.08805/) (2024)
- [Catheter Detection and Segmentation in X-ray Images via Multi-task Learning](https://mlsanity.com/paper/2503.02717/) (2025)
- [Generalizing Segmentation Foundation Model Under Sim-to-real Domain-shift for Guidewire Segmentation in X-ray Fluoroscopy](https://mlsanity.com/paper/2410.07460/) (2024)
- [Few-Shot Video Object Segmentation in X-Ray Angiography Using Local Matching and Spatio-Temporal Consistency Loss](https://mlsanity.com/paper/2601.00988/) (2026)
- [Towards Real-Time Autonomous Navigation: Transformer-Based Catheter Tip Tracking in Fluoroscopy](https://mlsanity.com/paper/2605.14253/) (2026)
- [Automated Precision Localization of Peripherally Inserted Central Catheter Tip through Model-Agnostic Multi-Stage Networks](https://mlsanity.com/paper/2206.06730/) (2022)
- [Guide3D: A Bi-planar X-ray Dataset for 3D Shape Reconstruction](https://mlsanity.com/paper/2410.22224/) (2024)
- [SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation](https://mlsanity.com/paper/2501.04515/) (2025)
- [VDSB-GWSyn: Diffusion Schr"odinger Bridge for Controllable and Anatomically Feasible Guidewire Synthesis in Coronary Angiography](https://mlsanity.com/paper/2606.00109/) (2026)
- [Dual-Part Multi-Lateral Branched Network for Multi-Class Segmentation in Cardiovascular Catheterization Angiograms](https://mlsanity.com/paper/2609.04590/) (2026)

## Abstract (arXiv)

Augmenting X-ray imaging with 3D roadmap to improve guidance is a common strategy. Such approaches benefit from automated analysis of the X-ray images, such as the automatic detection and tracking of instruments. In this paper, we propose a real-time method to segment the catheter and guidewire in 2D X-ray fluoroscopic sequences. The method is based on deep convolutional neural networks. The network takes as input the current image and the three previous ones, and segments the catheter and guidewire in the current image. Subsequently, a centerline model of the catheter is constructed from the segmented image. A small set of annotated data combined with data augmentation is used to train the network. We trained the method on images from 182 X-ray sequences from 23 different interventions. On a testing set with images of 55 X-ray sequences from 5 other interventions, a median centerline distance error of 0.2 mm and a median tip distance error of 0.9 mm was obtained. The segmentation of the instruments in 2D X-ray sequences is performed in a real-time fully-automatic manner.