Endotracheal Tube Detection and Segmentation in Chest Radiographs Using Synthetic Data

Frid-Adar, Maayan; Amer, Rula; Greenspan, Hayit

doi:10.1007/978-3-030-32226-7_87

Cited by 26 publications

(14 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an initial study, Lakhani et al [20] utilized a global CNN classifier for binary assessment of ETT position without explicit localization. Frid-Adar et al [21] localizes the entire course of the ETT using a fully-convolutional CNN model for semantic segmentation trained with a combination of both real and synthetic data, however does not characterize the location of the distal ETT tip relative to the carina. Finally, Huo et al [22] uses traditional computer vision techniques to assess ETT position with a sensitivity of 0.85 for ETT detection and an accuracy of 0.81 for ETT localization within 10 mm of ground-truth annotations.…”

Section: Discussionmentioning

confidence: 99%

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach

Kara¹,

Akers

Chang

2021

J Digit Imaging

View full text Add to dashboard Cite

Rapid and accurate assessment of endotracheal tube (ETT) location is essential in the intensive care unit (ICU) setting, where timely identification of a mispositioned support device may prevent significant patient morbidity and mortality. This study proposes a series of deep learning-based algorithms which together iteratively identify and localize the position of an ETT relative to the carina on chest radiographs. Using the open-source MIMIC Chest X-Ray (MIMIC-CXR) dataset, a total of 16,000 patients were identified (8000 patients with an ETT and 8000 patients without an ETT). Three different convolutional neural network (CNN) algorithms were created. First, a regression loss function CNN was trained to estimate the coordinate location of the carina, which was then used to crop the original radiograph to the distal trachea and proximal bronchi. Second, a classifier CNN was trained using the cropped inputs to determine the presence or absence of an ETT. Finally, for radiographs containing an ETT, a third regression CNN was trained to both refine the coordinate location of the carina and identify the location of the distal ETT tip. Model accuracy was assessed by comparing the absolute distance of prediction and ground-truth coordinates as well as CNN predictions relative to measurements documented in original radiologic reports. Upon five-fold cross validation, binary classification for the presence or absence of ETT demonstrated an accuracy, sensitivity, specificity, PPV, NPV, and AUC of 97.14%, 97.37%, 96.89%, 97.12%, 97.15%, and 99.58% respectively. CNN predicted coordinate location of the carina, and distal ETT tip was estimated within a median error of 0.46 cm and 0.60 cm from ground-truth annotations respectively. Overall final CNN assessment of distance between the carina and distal ETT tip was predicted within a median error of 0.60 cm from manual ground-truth annotations, and a median error of 0.66 cm from measurements documented in the original radiology reports. A serial cascaded CNN approach demonstrates high accuracy for both identification and localization of ETT tip and carina on chest radiographs. High performance of the proposed multi-step strategy is in part related to iterative refinement of coordinate localization as well as explicit image cropping which focuses algorithm attention to key anatomic regions of interest.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach

Kara¹,

Akers

Chang

2021

J Digit Imaging

View full text Add to dashboard Cite

show abstract

“…Besides, 3D-HED and its variant were applied for vascular boundary detection [24]. Other scenarios such as using synthetic data to improve endotracheal tube segmentation [15]. Cross-modality domain adaptation framework with adversarial learning which dealt with the domain shift in segmenting biomedical images including ascending aorta was also proposed [13].…”

Section: Learning-based Methodsmentioning

confidence: 99%

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

Wang

Liu

et al. 2019

Preprint

View full text Add to dashboard Cite

Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related diseases. But automatic tubular structure segmentation in CT scans is a challenging problem, due to issues such as poor contrast, noise and complicated background. A tubular structure usually has a cylinder-like shape which can be well represented by its skeleton and cross-sectional radii (scales). Inspired by this, we propose a geometryaware tubular structure segmentation method, Deep Distance Transform (DDT), which combines intuitions from the classical distance transform for skeletonization and modern deep segmentation networks. DDT first learns a multitask network to predict a segmentation mask for a tubular structure and a distance map. Each value in the map represents the distance from each tubular structure voxel to the tubular structure surface. Then the segmentation mask is refined by leveraging the shape prior reconstructed from the distance map. We apply our DDT on six medical image datasets. The experiments show that (1) DDT can boost tubular structure segmentation performance significantly (e.g., over 13% improvement measured by DSC for pancreatic duct segmentation), and (2) DDT additionally provides a geometrical measurement for a tubular structure, which is important for clinical diagnosis (e.g., the crosssectional scale of a pancreatic duct can be an indicator for pancreatic cancer). * This work was done when X. Wei and J. Chen did internship at JHU.† Equal Contribution.

show abstract

“…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. 36 Since the data set now is publicly accessible, we expect to see more research regarding assessment of catheters and tubes on radiographs in the near future.…”

Section: Assessment Of Lines and Tubesmentioning

confidence: 99%

Artificial Intelligence Solutions for Analysis of X-ray Images

et al. 2020

View full text Add to dashboard Cite

Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.

show abstract

Endotracheal Tube Detection and Segmentation in Chest Radiographs Using Synthetic Data

Cited by 26 publications

References 8 publications

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach

Deep Distance Transform for Tubular Structure Segmentation in CT Scans

Artificial Intelligence Solutions for Analysis of X-ray Images

Contact Info

Product

Resources

About