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Automatic 3D Segmentation of Renal Cysts in CT

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Information Technologies in Medicine (ITiB 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 471))

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Abstract

A fully automatic methodology for renal cysts detection and segmentation in abdominal computed tomography is presented in this paper. The segmentation workflow begins with the lungs segmentation followed by the kidneys extraction using marker controlled watershed algorithm. Detection of candidate cysts employs the artificial neural network classifier supplied by shape-related 3D object features. Anisotropic diffusion filtering and hybrid level set method are used at the fine segmentation stage. During the evaluation 23 out of 25 cysts delineated by an expert within 16 studies were detected correctly. The fine segmentation stage resulted in a \(92.3\,\%\) sensitivity and \(93.2\,\%\) Dice index combined over all detected cases.

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Notes

  1. 1.

    In case a metric size is used, the structuring element depends on the CT voxel size.

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Acknowledgments

This research was supported by the Polish National Science Center (NCN) grant No. UMO-2012/05/B/ST7/02136.

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Authors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, Zabrze, Poland

    Pawel Badura, Wojciech Wieclawek & Bartlomiej Pycinski

Authors
  1. Pawel Badura
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  2. Wojciech Wieclawek
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  3. Bartlomiej Pycinski
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Corresponding author

Correspondence to Pawel Badura .

Editor information

Editors and Affiliations

  1. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Ewa Piętka

  2. Fac of Biomed Engg, ul. Roosevelta 40, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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Badura, P., Wieclawek, W., Pycinski, B. (2016). Automatic 3D Segmentation of Renal Cysts in CT. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-319-39796-2_13

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  • DOI: https://doi.org/10.1007/978-3-319-39796-2_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39795-5

  • Online ISBN: 978-3-319-39796-2

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