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Learning to Segment Microscopy Images with Lazy Labels

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Computer Vision – ECCV 2020 Workshops (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12535))

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Abstract

The need for labour intensive pixel-wise annotation is a major limitation of many fully supervised learning methods for segmenting bioimages that can contain numerous object instances with thin separations. In this paper, we introduce a deep convolutional neural network for microscopy image segmentation. Annotation issues are circumvented by letting the network being trainable on coarse labels combined with only a very small number of images with pixel-wise annotations. We call this new labelling strategy ‘lazy’ labels. Image segmentation is stratified into three connected tasks: rough inner region detection, object separation and pixel-wise segmentation. These tasks are learned in an end-to-end multi-task learning framework. The method is demonstrated on two microscopy datasets, where we show that the model gives accurate segmentation results even if exact boundary labels are missing for a majority of annotated data. It brings more flexibility and efficiency for training deep neural networks that are data hungry and is applicable to biomedical images with poor contrast at the object boundaries or with diverse textures and repeated patterns.

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Acknowledgments

RK and CBS acknowledge support from the EPSRC grant EP/T003553/1. CBS additionally acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, the Philip Leverhulme Prize, the EPSRC grant EP/S026045/1, the EPSRC Centre Nr. EP/N014588/1, the RISE projects CHiPS and NoMADS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute, Royal Society Wolfson fellowship. AB and NP acknowledge support from the EU Horizon 2020 research and innovation programme NoMADS (Marie Skłodowska-Curie grant agreement No. 777826).

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Authors and Affiliations

  1. DAMTP, University of Cambridge, Cambridge, CB3 0WA, UK

    Rihuan Ke & Carola-Bibiane Schönlieb

  2. LaBR, University of Bordeaux, UMR 5800, 33400, Talence, France

    Aurélie Bugeau

  3. CNRS, University of Bordeaux, IMB, UMR 5251, 33400, Talence, France

    Nicolas Papadakis

  4. Unilever R&D Colworth, Bedford, MK44 1LQ, UK

    Peter Schuetz

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  1. Rihuan Ke
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  2. Aurélie Bugeau
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  5. Carola-Bibiane Schönlieb
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Correspondence to Rihuan Ke .

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Editors and Affiliations

  1. University of Clermont Auvergne, Clermont Ferrand, France

    Adrien Bartoli

  2. Università degli Studi di Udine, Udine, Italy

    Andrea Fusiello

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Ke, R., Bugeau, A., Papadakis, N., Schuetz, P., Schönlieb, CB. (2020). Learning to Segment Microscopy Images with Lazy Labels. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-66415-2_27

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