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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603

Abstract

Background

Accurate identification of vascular lumen region founded the base of bubble detection and bubble grading, which played a significant role in the detection of vascular gas emboli for the diagnosis of decompression sickness.

Objective

To assist in the detection of vascular bubbles, it is crucial to develop an automatic algorithm that could identify vascular lumen areas in ultrasound videos with the interference of bubble presence.

Methods

This article proposed an automated vascular lumen region recognition (VLRR) algorithm that could sketch the accurate boundary between vessel lumen and tissues from dynamic 2D ultrasound videos. It adopts 2D ultrasound videos of the lumen area as input and outputs the frames with circled vascular lumen boundary of the videos. Normalized cross-correlation method, distance transform technique, and region growing technique were adopted in this algorithm.

Results

A double-blind test was carried out to test the recognition accuracy of the algorithm on 180 samples in the images of 6 different grades of bubble videos, during which, intersection over union and pixel accuracy were adopted as evaluation metrics. The average IOU on the images of different bubble grades reached 0.76. The mean PA on 6 of the images of bubble grades reached 0.82.

Conclusion

It is concluded that the proposed method could identify the vascular lumen with high accuracy, potentially applicable to assist clinicians in the measurement of the severity of vascular gas emboli in clinics.

This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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References

  1. MahonR.T. RegisD.P. Decompression and decompression sickness.Compr. Physiol.2014431157117510.1002/cphy.c13003924944033
    [Google Scholar]
  2. VannR.D. ButlerF.K. MitchellS.J. MoonR.E. Decompression illness.Lancet2011377976015316410.1016/S0140‑6736(10)61085‑921215883
    [Google Scholar]
  3. EftedalO. BrubakkA.O. Detecting intravascular gas bubbles in ultrasonic images.Med. Biol. Eng. Comput.199331662763310.1007/BF024418128145590
    [Google Scholar]
  4. NeumanT.S. Arterial gas embolism and decompression sickness.News Physiol. Sci.2002172778111909997
    [Google Scholar]
  5. CialoniD. PieriM. BalestraC. MarroniA. Dive risk factors, gas bubble formation, and decompression illness in recreational SCUBA diving: analysis of DAN Europe DSL data base.Front. Psychol.20178158710.3389/fpsyg.2017.0158728974936
    [Google Scholar]
  6. BloggLS GennserM MøllerløkkenA BrubakkAO Ultrasound detection of vascular decompression bubbles: The influence of new technology and considerations on bubble load.Diving Hyperb Med20144413544
    [Google Scholar]
  7. EftedalO. BrubakkA. Agreement between trained and untrained observers in grading intravascular bubble signals in ultrasonic images.Undersea & hyperbaric medicine: journal of the Undersea and Hyperbaric Medical Society Inc.1997244293299
    [Google Scholar]
  8. ChappellM.A. PayneS.J. A method for the automated detection of venous gas bubbles in humans using empirical mode decomposition.Ann. Biomed. Eng.200533101411142110.1007/s10439‑005‑6045‑816240089
    [Google Scholar]
  9. IzadifarZ. BabynP. ChapmanD. Ultrasound cavitation/microbubble detection and medical applications.J. Med. Biol. Eng.201939325927610.1007/s40846‑018‑0391‑0
    [Google Scholar]
  10. JiangB. ChenA. BharatS. ZhengM. Automatic ultrasound vessel segmentation with deep spatiotemporal context learning.Simplifying Medical Ultrasound: Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021,Strasbourg, France, September 27, 2021, Proceedings,pp. 3–13.10.1007/978‑3‑030‑87583‑1_1
    [Google Scholar]
  11. LoizouC.P. A review of ultrasound common carotid artery image and video segmentation techniques.Med. Biol. Eng. Comput.201452121073109310.1007/s11517‑014‑1203‑525284219
    [Google Scholar]
  12. GuoQ. LiuH. WangL. LvH. WangY. The design and implementation of decompression sickness bubble detection system based on dynamic ultrasound images.J. Med. Imaging Health Inform.20188590090610.1166/jmihi.2018.2389
    [Google Scholar]
  13. RezatofighiH. TsoiN. GwakJ. SadeghianA. ReidI. SavareseS. Generalized intersection over union: A metric and a loss for bounding box regression.2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)201910.1109/CVPR.2019.00075
    [Google Scholar]
  14. BrubakkA.O. EftedalO. Comparison of three different ultrasonic methods for quantification of intravascular gas bubbles.Undersea Hyperb. Med.200128313113612067148
    [Google Scholar]
  15. HenriquesJ.F. CaseiroR. MartinsP. BatistaJ. High-speed tracking with kernelized correlation filters.IEEE Trans. Pattern Anal. Mach. Intell.201537358359610.1109/TPAMI.2014.234539026353263
    [Google Scholar]
  16. HaddadR.A. AkansuA.N. A class of fast Gaussian binomial filters for speech and image processing.IEEE Trans. Signal Process.199139372372710.1109/78.80892
    [Google Scholar]
  17. DalalN. TriggsB. Histograms of oriented gradients for human detection.2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05)Ieee2005
    [Google Scholar]
  18. McConnellR.K. Method of and apparatus for pattern recognition.Patents US4567610A1986.
  19. ZhaoF. HuangQ. GaoW. Image matching by normalized cross-correlation.2006 IEEE international conference on acoustics speech and signal processing proceedings.2006
    [Google Scholar]
  20. ChakiN. ShaikhS.H. SaeedK. ChakiN. ShaikhS.H. SaeedK. A comprehensive survey on image binarization techniques.Springer201410.1007/978‑81‑322‑1907‑1
    [Google Scholar]
  21. ShihF.Y.C. MitchellO.R. A mathematical morphology approach to Euclidean distance transformation.IEEE Trans. Image Process.19921219720410.1109/83.13659618296154
    [Google Scholar]
  22. BradskiG. KaehlerA. Learning OpenCV: Computer vision with the OpenCV library.O'Reilly Media, Inc.2008
    [Google Scholar]
  23. AdamsR. BischofL. Seeded region growing.IEEE Trans. Pattern Anal. Mach. Intell.199416664164710.1109/34.295913
    [Google Scholar]
  24. TangX. LiQ. Time-frequency analysis and wavelet transform.Beijing, ChinaScience Press2008
    [Google Scholar]
  25. TorralbaA. RussellB.C. YuenJ. LabelMe: Online Image Annotation and Applications.Proc. IEEE20109881467148410.1109/JPROC.2010.2050290
    [Google Scholar]
  26. HirschR. Exploring colour photography: A complete guide.Laurence King Publishing2005
    [Google Scholar]
  27. HurtadoJ.V. ValadaA. Semantic scene segmentation for robotics.Deep Learning for Robot Perception and Cognition.Chapter 12 IosifidisA. TefasA. Academic Press202227931110.1016/B978‑0‑32‑385787‑1.00017‑8
    [Google Scholar]
  28. StrutzT. The distance transform and its computation.arXiv:2106035032021
    [Google Scholar]
  29. SalihQ.A. RamliA.R. Region based segmentation technique and algorithms for 3D image.Proceedings of the Sixth International Symposium on Signal Processing and its Applications (CatNo01EX467),2001 13-16 Aug. 2001.
    [Google Scholar]
  30. KasoA. Computation of the normalized cross-correlation by fast Fourier transform.PLoS One2018139e020343410.1371/journal.pone.020343430235231
    [Google Scholar]
  31. KlibanovA.L. HughesM.S. VillanuevaF.S. Targeting and ultrasound imaging of microbubble-based contrast agents. Magnetic Resonance Materials in Physics.Biology and Medicine.19998177184
    [Google Scholar]
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  • Article Type:
    Research Article
Keyword(s): Automatic algorithm; Bubble detection; DCS; KCF; Tissue segmentation; Ultrasound videos
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