Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data

TL;DR

This paper introduces a deep learning approach for detecting and segmenting endotracheal tubes in chest X-rays, utilizing synthetic data for training to overcome annotation limitations, achieving high accuracy and quality in real images.

Contribution

The study presents a novel synthetic data generation method combined with a two-phase training process for ET tube detection and segmentation in chest radiographs.

Findings

AUC of 0.99 in classification

High-quality segmentation maps produced

Effective training with synthetic data

Abstract

Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET) tube is critical for the patient. In this study we propose a novel automated deep learning scheme for accurate detection and segmentation of the ET tubes. Development of automatic systems using deep learning networks for classification and segmentation require large annotated data which is not always available. Here we present an approach for synthesizing ET tubes in real X-ray images. We suggest a method for training the network, first with synthetic data and then with real X-ray images in a fine-tuning phase, which allows the network to train on thousands of cases without annotating any data. The proposed method was tested on 477 real chest radiographs from a public dataset and reached AUC of 0.99 in classifying the presence vs. absence of the ET tube, along with outputting high quality ET tube segmentation maps.