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Cell Segmentation Proposal Network for Microscopy Image Analysis

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Deep Learning and Data Labeling for Medical Applications (DLMIA 2016, LABELS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10008))

Abstract

Accurate cell segmentation is vital for the development of reliable microscopy image analysis methods. It is a very challenging problem due to low contrast, weak boundaries, and conjoined and overlapping cells; producing many ambiguous regions, which lower the performance of automated segmentation methods. Cell proposals provide an efficient way of exploiting both spatial and temporal context, which can be very helpful in many of these ambiguous regions. However, most proposal based microscopy image analysis methods rely on fairly simple proposal generation stage, limiting their performance. In this paper, we propose a convolutional neural network based method which provides cell segmentation proposals, which can be used for cell detection, segmentation and tracking. We evaluate our method on datasets from histology, fluorescence and phase contrast microscopy and show that it outperforms state of the art cell detection and segmentation methods.

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Notes

  1. 1.

    http://www.robots.ox.ac.uk/~vgg/software/cell_detection/.

  2. 2.

    http://codesolorzano.com/celltrackingchallenge/Cell_Tracking_Challenge/KTH-SE.html.

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Author information

Authors and Affiliations

  1. Center for Machine Vision and Signal Analysis, University of Oulu, Finland

    Saad Ullah Akram & Janne Heikkilä

  2. Biocenter Oulu, Oulu, Finland

    Saad Ullah Akram & Lauri Eklund

  3. Department of Computer Science, Aalto University, Espoo, Finland

    Juho Kannala

  4. Faculty of Biochemistry and Molecular Medicine, Oulu Center for Cell-Matrix Research, University of Oulu, Finland

    Lauri Eklund

Authors
  1. Saad Ullah Akram
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  2. Juho Kannala
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  3. Lauri Eklund
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  4. Janne Heikkilä
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Corresponding author

Correspondence to Saad Ullah Akram .

Editor information

Editors and Affiliations

  1. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  2. Technical University of Munich, Garching, Germany

    Diana Mateus

  3. Technical University of Munich, Garching, Germany

    Loïc Peter

  4. University of Queensland, St Lucia, Queensland, Australia

    Andrew Bradley

  5. Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  6. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  7. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  8. Instituto Superior Técnico, Lisbon, Portugal

    Jacinto C. Nascimento

  9. Delft University of Technology, Delft, The Netherlands

    Marco Loog

  10. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

  11. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  12. Google DeepMind, London, United Kingdom

    Julien Cornebise

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© 2016 Springer International Publishing AG

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Akram, S.U., Kannala, J., Eklund, L., Heikkilä, J. (2016). Cell Segmentation Proposal Network for Microscopy Image Analysis. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-46976-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46975-1

  • Online ISBN: 978-3-319-46976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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