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Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung

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Image Analysis and Recognition (ICIAR 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12132))

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Abstract

In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classification tasks and an ensemble-based model for the follow-up recommendation. This solution was evaluated within the LNDb 2020 medical imaging challenge and produced the best nodule segmentation result on the final leaderboard.

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Acknowledgments

We are grateful to xperience.ai for support of the research and Andrey Savchenko for his assistance in preparation of this paper.

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Authors and Affiliations

  1. Xperience.ai, Maxim Gorky street 262, 603155, Nizhny Novgorod, Russia

    Alexandr Rassadin

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  1. Alexandr Rassadin
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Correspondence to Alexandr Rassadin .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. University of Waterloo, Waterloo, ON, Canada

    Fakhri Karray

  3. University of Waterloo, Waterloo, ON, Canada

    Zhou Wang

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Rassadin, A. (2020). Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_37

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  • DOI: https://doi.org/10.1007/978-3-030-50516-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50515-8

  • Online ISBN: 978-3-030-50516-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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