Advertisement

HAPAN: Support Tool for Practicing Regional Anesthesia in Peripheral Nerves

  • J. A. Hernández-MurielEmail author
  • J. C. Mejía-Hernández
  • J. D. Echeverry-Correa
  • A. A. Orozco
  • D. Cárdenas-Peña
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)

Abstract

Ultrasound (US) medical imaging rises as a technique used to visualize nerve structures, among other applications. It has been used, typically, as a tool for assisting in the practice of peripheral nerve anesthesia. Due to its non-invasive nature, US may reduce the risk of injury to medical patients during surgical procedures. Despite its usefulness, it is challenging for anesthesiologists to perform the anesthesia process, mainly due to the presence of speckle and acoustic multiplicative noise, significantly degrading the image quality. Besides, the lack of homogeneity in the imaged structures disorients the anesthesiologist in the effective localization of the nerve structure. In this paper, we present the design and implementation of the software toolkit HAPAN (HAPAN is a Spanish acronym for Herramienta de Asistencia para la Práctica de Anestesia en Nervios periféricos-Assistance tool for the anesthesia of peripheral nerves.), developed in MATLAB, for the segmentation of different peripheral nerves in ultrasound images. HAPAN includes algorithms for automatic nerve segmentation based on appearance shape models, and image resolution enhancement.

Keywords

Peripheral nerves Regional anesthesia Support tool 

Notes

Acknowledgments

This work was developed for the research project 111074455958 funded by Colciencias. The authors also acknowledge the electrical engineering master program of Universidad Tecnologica de Pereira for supporting the research project development.

References

  1. 1.
    Chen, S.A., Ong, C.S., Hibino, N., Baschat, A.A., Garcia, J.R., Miller, J.L.: 3D printing of fetal heart using 3D ultrasound imaging data. Ultrasound Obstet. Gynecol. 52(6), 808–809 (2018)CrossRefGoogle Scholar
  2. 2.
    Daoud, M.I., Atallah, A.A., Awwad, F., Al-Najjar, M., Alazrai, R.: Automatic superpixel-based segmentation method for breast ultrasound images. Expert Syst. Appl. 121, 78–96 (2019)CrossRefGoogle Scholar
  3. 3.
    García, H.F., Giraldo, J.J., Álvarez, M.A., Orozco, Á.A., Salazar, D.: Peripheral nerve segmentation using speckle removal and bayesian shape models. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 387–394. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19390-8_44CrossRefGoogle Scholar
  4. 4.
    Giraldo, J.J., Álvarez, M.A., Orozco, Á.A.: Peripheral nerve segmentation using nonparametric Bayesian hierarchical clustering. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3101–3104. IEEE (2015)Google Scholar
  5. 5.
    González, J.G., Álvarez, M.A., Orozco, Á.A.: Automatic segmentation of nerve structures in ultrasound images using graph cuts and Gaussian processes. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3089–3092. IEEE (2015)Google Scholar
  6. 6.
    González, J.G., Álvarez, M.A., Orozco, Á.A.: Peripheral nerves segmentation in ultrasound images using non-linear wavelets and Gaussian processes. In: Paredes, R., Cardoso, J.S., Pardo, X.M. (eds.) IbPRIA 2015. LNCS, vol. 9117, pp. 603–611. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-19390-8_68CrossRefGoogle Scholar
  7. 7.
    Illanes, A., Esmaeili, N., Poudel, P., Balakrishnan, S., Friebe, M.: Parametrical modelling for texture characterizationa novel approach applied to ultrasound thyroid segmentation. PloS One 14(1), e0211215 (2019)CrossRefGoogle Scholar
  8. 8.
    Kim, B., Kim, K.C., Park, Y., Kwon, J.Y., Jang, J., Seo, J.K.: Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images. Physiol. Meas. 39(10), 105007 (2018)CrossRefGoogle Scholar
  9. 9.
    Liu, C., Liu, W., Xing, W.: A weighted edge-based level set method based on multi-local statistical information for noisy image segmentation. J. Vis. Commun. Image Represent. 59, 89–107 (2019)CrossRefGoogle Scholar
  10. 10.
    Ma, L., Kiyomatsu, H., Nakagawa, K., Wang, J., Kobayashi, E., Sakuma, I.: Accurate vessel segmentation in ultrasound images using a local-phase-based snake. Biomed. Sig. Process. Control 43, 236–243 (2018)CrossRefGoogle Scholar
  11. 11.
    Meiburger, K.M., Acharya, U.R., Molinari, F.: Automated localization and segmentation techniques for B-mode ultrasound images: a review. Comput. Biol. Med. 92, 210–235 (2018)CrossRefGoogle Scholar
  12. 12.
    Molinari, F., Caresio, C., Acharya, U.R., Mookiah, M.R.K., Minetto, M.A.: Advances in quantitative muscle ultrasonography using texture analysis of ultrasound images. Ultrasound Med. Biol. 41(9), 2520–2532 (2015)CrossRefGoogle Scholar
  13. 13.
    Moradi, M., Mahdavi, S.S., Guerrero, J., Rohling, R., Salcudean, S.E.: Ultrasound segmentation based on statistical unit-root test of B-scan radial intensity profiles. In: CMBES Proceedings, vol. 33, no. 1 (2018)Google Scholar
  14. 14.
    Nieuwveld, D., Mojica, V., Herrera, A., Pomés, J., Prats, A., Sala-Blanch, X.: Medial approach of ultrasound-guided costoclavicular plexus block and its effects on regional perfussion. Rev. Española de Anestesiología y Reanimación (Engl. Ed.) 64(4), 198–205 (2017)CrossRefGoogle Scholar
  15. 15.
    Smistad, E., Løvstakken, L.: Vessel detection in ultrasound images using deep convolutional neural networks. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 30–38. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46976-8_4CrossRefGoogle Scholar
  16. 16.
    Srivastava, A., Bhateja, V., Gupta, A., Gupta, A.: Non-local mean filter for suppression of speckle noise in ultrasound images. In: Satapathy, S.C., Bhateja, V., Das, S. (eds.) Smart Intelligent Computing and Applications. SIST, vol. 105, pp. 225–232. Springer, Singapore (2019).  https://doi.org/10.1007/978-981-13-1927-3_23CrossRefGoogle Scholar
  17. 17.
    Wang, H., Gao, X., Zhang, K., Li, J.: Single-image super-resolution using active-sampling Gaussian process regression. IEEE Trans. Image Process. 25(2), 935–948 (2016)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wang, W., Li, J., Jiang, Y., Xing, Y., Xu, X.: An automatic energy-based region growing method for ultrasound image segmentation. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 1553–1557. IEEE (2015)Google Scholar
  19. 19.
    Wieclawek, W., Rudzki, M., Wijata, A., Galinska, M.: Preliminary development of an automatic breast tumour segmentation algorithm from ultrasound volumetric images. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) ITIB 2018. AISC, vol. 762, pp. 77–88. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-91211-0_7CrossRefGoogle Scholar
  20. 20.
    Zhou, Y., Zang, H., Xu, S., He, H., Lu, J., Fang, H.: An iterative speckle filtering algorithm for ultrasound images based on Bayesian nonlocal means filter model. Biomed. Sig. Process. Control 48, 104–117 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • J. A. Hernández-Muriel
    • 1
    Email author
  • J. C. Mejía-Hernández
    • 1
  • J. D. Echeverry-Correa
    • 1
  • A. A. Orozco
    • 1
  • D. Cárdenas-Peña
    • 1
  1. 1.Automatic Research Group, Faculty of EngineeringsUniversidad Tecnológica de PereiraPereiraColombia

Personalised recommendations