Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
This addresses the need for fast and accurate intubation verification in emergency medical settings, though it is incremental as it builds on existing deep learning methods with a synthetic data approach.
The study tackled the problem of verifying correct intubation in emergency rooms by developing an automated deep learning scheme for detecting and segmenting endotracheal tubes in chest radiographs, achieving an AUC of 0.99 on a dataset of 477 real images.
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.