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Unsupervised Clustering Methods for Lung Perfusion Data Segmentation in Electrical Impedance Tomography

  • Conference paper
  • First Online:
IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering (CLAIB 2022, CBEB 2022)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 99))

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Abstract

In this work, we evaluated unsupervised clustering methods in segmenting the electrical impedance tomography image during the assessment of pulmonary perfusion by injection of hypertonic saline solution. In clustering the image pixels, we assume the existence of purely lung pixels (solely due to lung perfusion without effects from other organs) and hybrid pixels (which contain heart and lung effects together). We used data from 5 pigs to generate truth masks and assess the quality of clustering. Among the methods tested, the k-means with the cosine metric proved to be the best, as it obtained the 95% sensitivity median and the 90% specificity median. We prioritized minimizing the false negative cases and false positive cases, as it would overestimate regional pulmonary perfusion.

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Acknowledgements

The authors would like to acknowledge the contributions of the funding agency CNPq for the financial support of this research and the Medical Investigations Laboratory (LIM-09) from the Faculty of Medicine of University of São Paulo (FMUSP) for providing the dataset.

Author information

Authors and Affiliations

  1. Institute of Science and Technology - ICT, Federal University of São Paulo - UNIFESP, São José dos Campos, Brazil

    Arthur S. Ribeiro

  2. Electronics Engineering Division, Aeronautics Institute of Technology - ITA, São José dos Campos, São Paulo, Brazil

    Yu H. W. Xia, Mônica M. S. Matsumoto & Marcus H. Victor Jr.

Authors
  1. Arthur S. Ribeiro
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  2. Yu H. W. Xia
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  3. Mônica M. S. Matsumoto
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  4. Marcus H. Victor Jr.
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Corresponding author

Correspondence to Arthur S. Ribeiro .

Editor information

Editors and Affiliations

  1. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Jefferson Luiz Brum Marques

  2. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Cesar Ramos Rodrigues

  3. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Daniela Ota Hisayasu Suzuki

  4. Institute of Biomedical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    José Marino Neto

  5. Institute of Biomedical Engineering, Department of Electrical and Electronic Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Renato García Ojeda

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The authors declare that they have no conflict of interest.

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Ribeiro, A.S., Xia, Y.H.W., Matsumoto, M.M.S., Victor, M.H. (2024). Unsupervised Clustering Methods for Lung Perfusion Data Segmentation in Electrical Impedance Tomography. In: Marques, J.L.B., Rodrigues, C.R., Suzuki, D.O.H., Marino Neto, J., García Ojeda, R. (eds) IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. CLAIB CBEB 2022 2022. IFMBE Proceedings, vol 99. Springer, Cham. https://doi.org/10.1007/978-3-031-49404-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-49404-8_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49403-1

  • Online ISBN: 978-3-031-49404-8

  • eBook Packages: EngineeringEngineering (R0)

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