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A Multi-scale Self-supervision Method for Improving Cell Nuclei Segmentation in Pathological Tissues

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Medical Image Understanding and Analysis (MIUA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13413))

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Abstract

Nuclei detection and segmentation in histopathological images is a prerequisite step for quantitative analysis including morphological shape and size to help in identifying cancer prognosis. Digital pathology field aims to improve the quality of cancer diagnosis and has helped pathologists to reduce their efforts and time. Different deep learning architectures are widely used recently in Digital pathology field, yielding promising results in different problems. However, Deep convolutional neural networks (CNNs) need a large subset of labelled data that are not easily available all the time in the field of digital pathology. On the other hand, self-supervision methods are frequently used in different problems with the aim to overcome the lack of labelled data. In this study, we examine the impact of using self-supervision approaches on the segmentation problem. Also, we introduce a new multi-scale self-supervision method based on the zooming factor of the tissue. We compare the proposed method to the basic segmentation method and other popular self-supervision approaches that are used in other applications. The proposed Multi-scale self-supervision approach is applied on two publicly available pathology datasets. The results showed that the proposed approach outperforms Baseline U-Net by 0.2% and 0.02% for nuclei segmentation–mean Aggregated Jaccard Index (AJI), in TNBC and MoNuSeg, respectively.

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Authors and Affiliations

  1. Medical Imaging and Image Processing Research Group, Center for Informatics Science, Nile University, Giza, Egypt

    Hesham Ali, Mustafa Elattar & Sahar Selim

  2. School of Information Technology and Computer Science, Nile University, Giza, Egypt

    Hesham Ali, Mustafa Elattar & Sahar Selim

Authors
  1. Hesham Ali
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  2. Mustafa Elattar
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  3. Sahar Selim
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Correspondence to Hesham Ali .

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Editors and Affiliations

  1. Imperial College London, London, UK

    Guang Yang

  2. University of Cambridge, Cambridge, UK

    Angelica Aviles-Rivero

  3. University of Cambridge, Cambridge, UK

    Michael Roberts

  4. University of Cambridge, Cambridge, UK

    Carola-Bibiane Schönlieb

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Ali, H., Elattar, M., Selim, S. (2022). A Multi-scale Self-supervision Method for Improving Cell Nuclei Segmentation in Pathological Tissues. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_55

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  • DOI: https://doi.org/10.1007/978-3-031-12053-4_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12052-7

  • Online ISBN: 978-3-031-12053-4

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