Literature DB >> 25467807

Automated detection of endotracheal tubes in paediatric chest radiographs.

E-Fong Kao1, Twei-Shiun Jaw2, Chun-Wei Li3, Ming-Chung Chou3, Gin-Chung Liu2.   

Abstract

The aim of this study was to develop an automated method for the detection of endotracheal tube and location of its tip in paediatric chest radiographs. In this method, a seed point was first determined from the line crossing the cervical region and a line path was traced from the seed point. Two features, Lmax and C, were determined from the path and were combined to detect the existence of the endotracheal tube. Multiple thresholds applied to the line path were used to determine the candidate locations for the tip, and the most suitable location was selected from these candidates by analysing the image features. To evaluate the performance of detection of endotracheal tube existence, support vector machine was used to classify the images with and without endotracheal tubes on the basis of Lmax and C. The discriminant performance of the method was evaluated using receiver operating characteristic (ROC) analysis. To evaluate the precision of the detected tip locations, the tip locations in paediatric chest images were annotated by a radiologist. The distance (error) between the detected and annotated locations was used to evaluate detection precision for the tip location. The proposed method was evaluated using 528 images with endotracheal tubes and 816 images without endotracheal tubes. The discriminant performance in this study, evaluated as Az (area under the ROC curve), for detecting the existence of endotracheal tubes on the basis of the two features was 0.943±0.009, and the detection error of the tip location was 1.89±2.01mm. The proposed method obtained high performance results and could be useful for detecting the malposition of endotracheal tubes in paediatric chest radiographs.
Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords:  Computer-aided detection; Endotracheal tube; Paediatric chest radiograph

Mesh:

Year:  2014        PMID: 25467807     DOI: 10.1016/j.cmpb.2014.10.009

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  8 in total

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

Authors:  X Yi; Scott Adams; Paul Babyn; Abdul Elnajmi
Journal:  J Digit Imaging       Date:  2020-02       Impact factor: 4.056

Review 2.  Computer-aided Assessment of Catheters and Tubes on Radiographs: How Good Is Artificial Intelligence for Assessment?

Authors:  Xin Yi; Scott J Adams; Robert D E Henderson; Paul Babyn
Journal:  Radiol Artif Intell       Date:  2020-01-29

3.  Endotracheal tubes positioning detection in adult portable chest radiography for intensive care unit.

Authors:  Sheng Chen; Min Zhang; Liping Yao; Wentao Xu
Journal:  Int J Comput Assist Radiol Surg       Date:  2016-06-14       Impact factor: 2.924

4.  Detecting Endotracheal Tube and Carina on Portable Supine Chest Radiographs Using One-Stage Detector with a Coarse-to-Fine Attention.

Authors:  Liang-Kai Mao; Min-Hsin Huang; Chao-Han Lai; Yung-Nien Sun; Chi-Yeh Chen
Journal:  Diagnostics (Basel)       Date:  2022-08-07

5.  Endotracheal Tube Position Assessment on Chest Radiographs Using Deep Learning.

Authors:  Paras Lakhani; Adam Flanders; Richard Gorniak
Journal:  Radiol Artif Intell       Date:  2020-11-18

6.  Automatic Detection and Classification of Multiple Catheters in Neonatal Radiographs with Deep Learning.

Authors:  Robert D E Henderson; Xin Yi; Scott J Adams; Paul Babyn
Journal:  J Digit Imaging       Date:  2021-06-25       Impact factor: 4.903

7.  Deep Convolutional Neural Networks for Endotracheal Tube Position and X-ray Image Classification: Challenges and Opportunities.

Authors:  Paras Lakhani
Journal:  J Digit Imaging       Date:  2017-08       Impact factor: 4.056

8.  Unplanned revision spinal surgery within a week: a retrospective analysis of surgical causes.

Authors:  Tsung-Ting Tsai; Sheng-Hsun Lee; Chi-Chien Niu; Po-Liang Lai; Lih-Huei Chen; Wen-Jer Chen
Journal:  BMC Musculoskelet Disord       Date:  2016-01-15       Impact factor: 2.362

  8 in total

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