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

  • Alexandr RassadinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12132)

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.

Keywords

Deep learning Medical imaging Semantic segmentation U-Net 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Xperience.aiNizhny NovgorodRussia

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