ArticleJournal of digital imaging2020
Automatic Catheter and Tube Detection in Pediatric X-ray Images Using a Scale-Recurrent Network and Synthetic Data.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pediatric chest radiograph interpretation: how far has artificial intelligence come? A systematic literature review.Pediatric radiology · 2022Pooled it
- A two-stage deep learning pipeline for automated detection and localization of endotracheal tubes on pediatric chest radiographs: a pilot study.Pediatric radiology · 2026Article
- Review of Artificial Intelligence for Automated Assessment of Lines, Drains, and Airways on Pediatric and Adult Chest Radiographs.Pediatric reports · 2026Review
- Pediatrics 4.0: the Transformative Impacts of the Latest Industrial Revolution on Pediatrics.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Towards multimodal visualization of esophageal motility: fusion of manometry, impedance, and videofluoroscopic image sequences.International journal of computer assisted radiology and surgery · 2025Article
- Anatomically Guided Deep Learning System for Right Internal Jugular Line (RIJL) Segmentation and Tip Localization in Chest X-Ray.Life (Basel, Switzerland) · 2025Article
- Supervised representation learning based on various levels of pediatric radiographic views for transfer learning.Scientific reports · 2024Article
- A principled representation of elongated structures using heatmaps.Scientific reports · 2023Article
- Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases.Scientific reports · 2023Article
- Multi-mode information fusion navigation system for robot-assisted vascular interventional surgery.BMC surgery · 2023Article
- An Artificial Neural Network for Nasogastric Tube Position Decision Support.Radiology. Artificial intelligence · 2023Article
- A Review of Synthetic Image Data and Its Use in Computer Vision.Journal of imaging · 2022Review
- Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.Pediatric radiology · 2022Review
- Combination of UNet++ and ResNeSt to classify chronic inflammation of the choledochal cystic wall in patients with pancreaticobiliary maljunction.The British journal of radiology · 2022Article
- Tubular shape aware data generation for segmentation in medical imaging.International journal of computer assisted radiology and surgery · 2022Article
- Assessment of germinal matrix hemorrhage on head ultrasound with deep learning algorithms.Pediatric radiology · 2022Article
- Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.Journal of digital imaging · 2021Article
- Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?Radiology. Artificial intelligence · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Catheters are commonly inserted life supporting devices. Because serious complications can arise from malpositioned catheters, X-ray images are used to assess the position of a catheter immediately after placement. Previous computer vision approaches to detect catheters on X-ray images were either rule-based or only capable of processing a limited number or type of catheters projecting over the chest. With the resurgence of deep learning, supervised training approaches are beginning to show promising results. However, dense annotation maps are required, and the work of a human annotator is difficult to scale. In this work, we propose an automatic approach for detection of catheters and tubes on pediatric X-ray images. We propose a simple way of synthesizing catheters on X-ray images to generate a training dataset by exploiting the fact that catheters are essentially tubular structures with various cross sectional profiles. Further, we develop a UNet-style segmentation network with a recurrent module that can process inputs at multiple scales and iteratively refine the detection result. 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, with F
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.