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Medical Image Segmentation Using Deep Learning

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Deep Learning in Healthcare

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 171))

Abstract

This chapter aims at providing an introduction to deep learning-based medical image segmentation. First, the reader is guided through the inherent challenges of medical image segmentation, for which actual approaches to overcome those limitations are discussed. Secondly, supervised and semi-supervised architectures are described, where encoder-decoder type networks are the most widely employed ones. Nonetheless, generative adversarial network-based semi-supervised approaches have recently gained the attention of the scientific community. The shift from traditional 2D to 3D architectures is also discussed, as well as the most common loss functions to improve the performance of medical image segmentation approaches. Finally, some future trends and conclusion are described.

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Authors and Affiliations

  1. Vicomtech, San Sebastián, Spain

    Karen López-Linares Román, María Inmaculada García Ocaña, Nerea Lete Urzelai & Iván Macía Oliver

  2. Universitat Pompeu Fabra, Barcelona, Spain

    Miguel Ángel González Ballester

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  1. Karen López-Linares Román
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Correspondence to Karen López-Linares Román .

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  1. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Yen-Wei Chen

  2. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, NSW, Australia

    Lakhmi C. Jain

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López-Linares Román, K., García Ocaña, M.I., Lete Urzelai, N., González Ballester, M.Á., Macía Oliver, I. (2020). Medical Image Segmentation Using Deep Learning. In: Chen, YW., Jain, L. (eds) Deep Learning in Healthcare. Intelligent Systems Reference Library, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-030-32606-7_2

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