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

TL;DR

This paper introduces a novel deep learning method using synthetic data and a scale-recurrent network to improve catheter detection in pediatric X-ray images, addressing annotation challenges and enhancing accuracy.

Contribution

The paper presents a new approach combining synthetic catheter data generation with a scale-recurrent network for improved detection in pediatric X-rays.

Findings

Effective detection of catheters in pediatric X-rays

High precision and recall achieved

Synthetic data improves training efficiency

Abstract

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.