Skip to main content
Log in

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

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

To present an automated method for detecting endotracheal (ET) tubes and marking their tips in portable chest radiography (CXR) for intensive care units (ICUs).

Methods

In this method, the lung region is first estimated and then the spine is detected between the right lung and the left lung. Because medical tubes are inserted into the body through the throat, the region of interest (ROI) is obtained across the spine. A seed point is determined in the cervical region of the ROI, and then the line path is selected from the seed point. In order to detect ET tubes, the ICU CXR image is preprocessed by contrast-limited adaptive histogram equalization. Then, a feature-based threshold method is applied to the line path to determine the tip location. A comparison to the method by use of Hough transform is also presented. The distance (error) between the detected locations and the locations annotated by a radiologist is used to evaluate the detection precision for the tip location.

Results

The proposed method is evaluated using 44 images with ET tubes and 43 images without ET tubes. The discriminant performance for detecting the existence of ET tubes in this study was 95 %, and the average of detection error for the tip location was approximately 2.5 mm.

Conclusions

The proposed method could be useful for detecting malpositioned ET tubes in ICU CXRs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Trotman-Dickenson BM (2003) Radiology in the intensive care unit (part I). J Intens Care Med 18:198–210

    Article  Google Scholar 

  2. Blayney MP, Logan DR (1994) First thoracic vertebral body as reference for endotracheal tube placement. Arch Dis Child-Fetal Neonatal Edn 71:32–35

    Article  Google Scholar 

  3. Behrens B, Rohr K, Stiehl H (2001) Using an extended hough transformation combined with a Kalmann filter to segment tubular structures in 3D medical images. In: Proceedings of workshop vision, modeling, and visualization. Aka GmbH, Heidelberg

  4. Staib LH, Duncan JS (1996) Model-based deformable surface finding for medical images. IEEE Trans Med Imaging 15:720–731

    Article  CAS  PubMed  Google Scholar 

  5. McInerney T, Terzopoulos D (1996) Deformable models in medical image analysis: a survey. Med Image Anal 1:91–108

    Article  CAS  PubMed  Google Scholar 

  6. Mitsuhashi R, Aoki Y, Kouno M, Hatsuda T (1993) Detection of a straight metal tube by snow search radar. In: Proceedings of geoscience and remote sensing symposium, 1993 (IGARSS’93). Better understanding of earth environment. IEEE, New York

  7. Kender JR, Kjeldsen R (1995) On seeing spaghetti: self-adjusting piecewise toroidal recognition of flexible extruded objects. IEEE Trans Pattern Anal Mach Intell 17:136–157

    Article  Google Scholar 

  8. Black JJM, Skinner DV (2000) Confirmation of correct endotracheal tube placement. J Accid Emerg Med 17:74–74

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sheng C, Li L, Pei W (2009) Automatic detection of supporting device positioning in intensive care unit radiography. Int J Med Robot Comp 5:332–340

    Article  Google Scholar 

  10. Ramakrishna B, Brown M, Goldin J, Cagnon C, Enzmann D (2011) Catheter detection and classification on chest radiographs: an automated prototype computer-aided detection (CAD) system for radiologists. In: Medical Imaging 2011: Computer-aided diagnosis, p 7963

  11. Ramakrishna B, Brown M, Goldin J, Cagnon C, Enzmann D (2012) An improved automatic computer aided tube detection and labeling system on chest radiographs. In: Medical imaging 2012: computer-aided diagnosis, p 8315

  12. Mercan CA, Celebi MS (2014) An approach for chest tube detection in chest radiographs. IET Image Process 8:122–129

    Article  Google Scholar 

  13. Kao EF, Jaw TS, Li CW, Chou MC, Liu GC (2015) Automated detection of endotracheal tubes in paediatric chest radiographs. Comput Methods Prog Biomed 118:1–10

    Article  Google Scholar 

  14. Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Proceedings of Graphics gems IV. Academic Press, San Diego, CA

  15. Saleh MD, Eswaran C, Mueen A (2011) An automated blood vessel segmentation algorithm using histogram equalization and automatic threshold selection. J Digit Imaging 24:564–572

    Article  PubMed  Google Scholar 

  16. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Sixth international conference on computer vision, pp 839–846

  17. Durand F, Dorsey J (2002) Fast bilateral filtering for the display of high-dynamic-range images. ACM Trans Graphics 21:257–266

    Google Scholar 

  18. Chaudhury KN, Sage D, Unser M (2011) Fast bilateral filtering using trigonometric range kernels. IEEE Trans Image Process 20:3376–3382

    Article  PubMed  Google Scholar 

  19. Al-Haj A, Amer A (2014) Secured telemedicine using region-based watermarking with tamper localization. J Digit Imaging 27:737–750

    Article  PubMed  PubMed Central  Google Scholar 

  20. Sofka M, Wetzl J, Birkbeck N, Zhang J, Kohlberger T, Kaftan J, Declerck J, Zhou SK (2011) Multi-stage learning for robust lung segmentation in challenging CT volumes. Lect Notes Comput Sci 6893:667–674

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Science Foundation of China (NSFC) 81101116, the Foundation of Hujiang (C14002), and the National Sciences Foundation of Shanghai (13ZR1410400).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Sheng Chen or Liping Yao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Zhang, M., Yao, L. et al. Endotracheal tubes positioning detection in adult portable chest radiography for intensive care unit. Int J CARS 11, 2049–2057 (2016). https://doi.org/10.1007/s11548-016-1430-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1430-3

Keywords

Navigation