# Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data

arXiv: https://arxiv.org/abs/1806.00921 · HTML: https://mlsanity.com/paper/1806.00921/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [Deep Learning for Chest X-ray Analysis: A Survey](https://mlsanity.com/paper/2103.08700/) (2021)
- [CathAction: A Benchmark for Endovascular Intervention Understanding](https://mlsanity.com/paper/2408.13126/) (2024)
- [Autonomous Catheterization with Open-source Simulator and Expert Trajectory](https://mlsanity.com/paper/2401.09059/) (2024)
- [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)
- [Attention on the Wires (AttWire): A Foundation Model for Detecting Devices and Catheters in X-ray Fluoroscopic Images](https://mlsanity.com/paper/2503.06190/) (2025)

## Abstract (arXiv)

Catheters are commonly inserted life supporting devices. X-ray images are used to assess the position of a catheter immediately after placement as serious complications can arise from malpositioned catheters. Previous computer vision approaches to detect catheters on X-ray images either relied on low-level cues that are not sufficiently robust or only capable of processing a limited number or type of catheters. With the resurgence of deep learning, supervised training approaches are begining to showing promising results. However, dense annotation maps are required, and the work of a human annotator is hard to scale. In this work, we proposed a simple way of synthesizing catheters on X-ray images and a scale recurrent network for catheter detection. By training on adult chest X-rays, the proposed network exhibits promising detection results on pediatric chest/abdomen X-rays in terms of both precision and recall.