Automatic Catheter and Tube Detection in Pediatric X-ray Images Using a Scale-Recurrent Network and Synthetic Data

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.

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

“…In a study by Yi et al [ 52 ], the authors used data with synthetic nasogastric tubes, endotracheal tubes and umbilical catheters to test their algorithm. The precision (i.e.…”

“…The precision (i.e. true positives / [true positives + false positives]) for their algorithm was 0.80 [ 52 ]. According to the authors, their work can contribute to the development of a system that detects all lines and catheters in X-ray images and could be used to prioritize images that show malpositioned lines and request urgent review by the radiologist [ 52 ].…”

An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging

“…Another study showed AI-based computer-aided design software flagged fewer false-positives per image in digital mammograms than FDA-approved computeraided design software, with no reduction in sensitivity [4]. And there have been noteworthy developments for AI in other areas too, such as the treatment planning of brain tumors and the identification of catheters and tubes in pediatric x-ray images [5,6].…”

Artificial Intelligence Solutions for Analysis of X-ray Images

“…Yi et al proposed a way to generate synthesized catheters on pediatric radiographs and used synthetic data to train a segmentation network for commonly seen catheters including endotracheal tubes, nasogastric tubes, and umbilical catheters. 33 A similar approach has been explored for the detection of endotracheal tubes on adult chest radiographs, achieving an AUROC of 0.99 in classifying the presence of endotracheal tubes. 34 A more in-depth review of the current status of AI for catheter placement assessment can be found in the study by Yi et al 35 In 2019, IBM and MICCAI cohosted the Multimodal Learning for Clinical Decision Support Challenge that was dedicated to catheter detection and classification.…”