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Annual Conference on Medical Image Understanding and Analysis

MIUA 2022: Medical Image Understanding and Analysis pp 547–561Cite as

From Astronomy to Histology: Adapting the FellWalker Algorithm to Deep Nuclear Instance Segmentation

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

Abstract

Accurate cell nuclei segmentation is necessary for subsequent histopathology image analysis, including tumour classification, grading and prognosis. Manually identifying cell nuclei is both difficult and time-consuming, with cell nuclei exhibiting dramatic differences in morphology and staining characteristics. Recently, significant advancements in automatic cell nuclei segmentation have been achieved using deep learning, with methods particularly successful in identifying cell nuclei from background tissue. However, delineating individual cell nuclei remains challenging, with often unclear boundaries between neighbouring nuclei. In this paper, we incorporate the FellWalker algorithm, originally developed for analysing molecular clouds, into a deep learning-based pipeline to perform instance cell nuclei segmentation. We evaluate our proposed method on the Lizard dataset, the largest publicly available nuclear segmentation dataset in digital pathology, and compare it against popular methods such as U-Net with Watershed and Mask R-CNN. Our proposed method consistently outperforms the other methods across dataset sizes, achieving an object Dice of 0.7876, F1 score of 0.8245 and Aggregated Jaccard Index of 0.6526. The flexible nature of our pipeline incorporating the FellWalker algorithm has the potential for broader application in biomedical image instance segmentation tasks.

Keywords

  • Digital pathology
  • Deep learning
  • Image processing
  • Instance segmentation

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Acknowledgements

This work was funded in part by the UK Research and Innovation Future Leaders Fellowship [MR/V023799/1], in part by the Medical Research Council [MC/PC/21013], in part by the European Research Council Innovative Medicines Initiative [DRAGON, H2020-JTI-IMI2 101005122], and in part by the AI for Health Imaging Award [CHAIMELEON, H2020-SC1-FA-DTS2019-1 952172]. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.

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Authors and Affiliations

  1. School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Michael Yeung

  2. National Heart and Lung Institute, Imperial College London, London, UK

    Michael Yeung & Guang Yang

  3. Department of Medical Genetics, University of Cambridge, Cambridge, UK

    Todd Watts

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  1. Michael Yeung
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Correspondence to Michael Yeung .

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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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Yeung, M., Watts, T., Yang, G. (2022). From Astronomy to Histology: Adapting the FellWalker Algorithm to Deep Nuclear Instance Segmentation. 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_41

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  • DOI: https://doi.org/10.1007/978-3-031-12053-4_41

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