Article

Unsupervised Cell Segmentation in Fluorescence Microscopy Images via Self-supervised Learning

Authors:

Karl RohrAuthors Info & Claims

Pattern Recognition and Artificial Intelligence: Third International Conference, ICPRAI 2022, Paris, France, June 1–3, 2022, Proceedings, Part I

Pages 236 - 247

https://doi.org/10.1007/978-3-031-09037-0_20

Published: 01 June 2022 Publication History

Abstract

Cell segmentation in microscopy images is challenging particularly when only few or no annotations available. Existing unsupervised deep learning-based segmentation methods rely on large data sets to train large networks, use synthetic training data, pre-trained networks for domain adaptation, or exploit labels to further train pre-trained networks. We propose an unsupervised deep learning method which is trained from scratch by self-supervised learning without requiring any segmentation labels. Our deep neural network generates an attention map and performs the auxiliary task in one network. The segmentation result is directly obtained from the network, and model selection is performed unsupervised based on the behavior of the loss function during training. We applied our approach to two different fluorescence microscopy data sets and achieved generally better or similar results than classical unsupervised segmentation methods. Furthermore, we compared our method to a supervised method trained with a different number of labels, as well as a semi-supervised version of our method where we select the model based on few annotations.

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Cited By

Hawchar MLefebvre J(2023)Leveraging Self-attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter ImagesMachine Learning in Medical Imaging10.1007/978-3-031-45673-2_25(247-256)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45673-2_25

Index Terms

Unsupervised Cell Segmentation in Fluorescence Microscopy Images via Self-supervised Learning
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

Pattern Recognition and Artificial Intelligence: Third International Conference, ICPRAI 2022, Paris, France, June 1–3, 2022, Proceedings, Part I

Jun 2022

718 pages

ISBN:978-3-031-09036-3

DOI:10.1007/978-3-031-09037-0

Editors:
Mounîm El Yacoubi
Télécom SudParis, Palaiseau, France
,
Eric Granger
École de Technologie Supérieure, Montreal, QC, Canada
,
Pong Chi Yuen
Hong Kong Baptist University, Kowloon, Kowloon, Hong Kong
,
Umapada Pal
Indian Statistical Institute, Kolkata, India
,
Nicole Vincent
Université Paris Cité, Paris, France

© Springer Nature Switzerland AG 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2022

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Cited By

Hawchar MLefebvre J(2023)Leveraging Self-attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter ImagesMachine Learning in Medical Imaging10.1007/978-3-031-45673-2_25(247-256)Online publication date: 8-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45673-2_25

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