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TernausNet

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Computer-Aided Analysis of Gastrointestinal Videos

Abstract

In this work we evaluate four different deep architectures for segmentation: U-Net Ronneberger et al. (2015), two modifications of TernausNet Iglovikov and Shvets (2018), and a modification of LinkNet called AlbuNet34 Chaurasia and Culurciello (2017); Shvets et al. (2018).

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References

  • Chaurasia, A., & Culurciello, E. (2017). Linknet: Exploiting encoder representations for efficient semantic segmentation. arXiv preprint arXiv:1707.03718.

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778).

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  • Iglovikov, V., & Shvets, A. (2018). TernausNet: U-net with VGG11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746.

  • Iglovikov, V. I., Rakhlin, A., Kalinin, A. A., & Shvets, A. A. (2018). Paediatric bone age assessment using deep convolutional neural networks. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (pp. 300–308). Springer.

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  • Iglovikov, V., Seferbekov, S., Buslaev, A., & Shvets, A. (2018, June). TernausNetV2: Fully convolutional network for instance segmentation. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.

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  • Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 234–241). Springer.

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  • Shvets, A. A., Rakhlin, A., Kalinin, A. A., & Iglovikov, V. I. (2018). Automatic instrument segmentation in robot-assisted surgery using deep learning. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE.

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  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

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Author information

Authors and Affiliations

  1. ODS.ai, San Francisco, USA

    Vladimir I. Iglovikov

  2. Massachusetts Institute of Technology - MIT, Cambridge, USA

    Alexey A. Shvets

Authors
  1. Vladimir I. Iglovikov
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  2. Alexey A. Shvets
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Corresponding author

Correspondence to Alexey A. Shvets .

Editor information

Editors and Affiliations

  1. Computer Vision Center and Computer Science Department, Autonomous University of Barcelona, Bellaterra, Barcelona, Spain

    Jorge Bernal

  2. ETIS UMR 8051 (CY Paris Cergy University, ENSEA, CNRS), École Nationale Supérieure de l’Electronique et de ses Applications, Cergy, France

    Aymeric Histace

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Iglovikov, V.I., Shvets, A.A. (2021). TernausNet. In: Bernal, J., Histace, A. (eds) Computer-Aided Analysis of Gastrointestinal Videos. Springer, Cham. https://doi.org/10.1007/978-3-030-64340-9_15

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  • DOI: https://doi.org/10.1007/978-3-030-64340-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64339-3

  • Online ISBN: 978-3-030-64340-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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