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Dilated ResFCN and SE-Unet for Polyp Segmentation

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Computer-Aided Analysis of Gastrointestinal Videos

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Abstract

Segmentation is one of the key enabling technologies in medical image analysis with a great variety of methods proposed (Histace et al. 2009; Zhang et al. 2010, 2013; Matuszewski et al. 2011). Methods based on deep learning, with the features learned directly from data rather than handcrafted, showed significant improvement in the quality of the segmentation including the analysis of colonoscopy images.

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Author information

Authors and Affiliations

  1. Computer Vision and Machine Learning (CVML) Group, School of Engineering, University of Central Lancashire, Preston, PR1 2HE, UK

    Yunbo Guo & Bogdan J. Matuszewski

  2. Suzhou Institute of Biomedical Engineering and Technology, China Academy of Sciences, Suzhou, China

    Yunbo Guo

Authors
  1. Yunbo Guo
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  2. Bogdan J. Matuszewski
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Corresponding author

Correspondence to Yunbo Guo .

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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Guo, Y., Matuszewski, B.J. (2021). Dilated ResFCN and SE-Unet for Polyp Segmentation. In: Bernal, J., Histace, A. (eds) Computer-Aided Analysis of Gastrointestinal Videos. Springer, Cham. https://doi.org/10.1007/978-3-030-64340-9_8

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  • DOI: https://doi.org/10.1007/978-3-030-64340-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64339-3

  • Online ISBN: 978-3-030-64340-9

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