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A Deep Learning Approach to Segment High-Content Images of the E. coli Bacteria

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Advanced Concepts for Intelligent Vision Systems (ACIVS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14124))

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Abstract

High-content imaging (HCI) has been used to study antimicrobial resistance in bacteria. Although cell segmentation is critical for accurately analyzing bacterial populations, existing HCI platforms were not optimized for bacterial cells. This study proposes a convolutional neural network-based approach utilizing transfer learning and fine-tuning to perform instance segmentation on fluorescence images of E. coli. The method uses the pre-trained EfficientNet as the encoder for feature extraction and U-Net for reconstructing the segmentation maps containing the cell cytoplasm and the cell instance boundary. Next, individual cells are separated using a marker-controlled watershed transformation. The EffNetB7-UNet yields the best performance with the highest F1-Score of 0.91 compared to other methods.

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Acknowledgment

This research is conducted under the Collaborative Research Agreement between the University of Oxford, the University of Cambridge, and the University of Science, Vietnam National University in Ho Chi Minh City, Viet Nam. We also want to acknowledge the support of the AISIA Research Lab during this paper.

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Authors and Affiliations

  1. AISIA Research Lab, Ho Chi Minh City, Vietnam

    Dat Q. Duong, Phuong Nhi Nguyen Kieu, Bao Le & Binh T. Nguyen

  2. University of Science, Ho Chi Minh City, Vietnam

    Dat Q. Duong, Phuong Nhi Nguyen Kieu, Tien K. Nguyen, Bao Le & Binh T. Nguyen

  3. Vietnam National University, Ho Chi Minh City, Vietnam

    Dat Q. Duong, Phuong Nhi Nguyen Kieu, Tien K. Nguyen, Bao Le & Binh T. Nguyen

  4. Nuffield Department of Medicine, University of Oxford, Oxford, UK

    Tuan-Anh Tran

  5. Department of Medicine, University of Cambridge, Cambridge, UK

    Stephen Baker

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  1. Dat Q. Duong
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  2. Tuan-Anh Tran
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  3. Phuong Nhi Nguyen Kieu
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  4. Tien K. Nguyen
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  5. Bao Le
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  7. Binh T. Nguyen
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Correspondence to Binh T. Nguyen .

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Editors and Affiliations

  1. DGA TA, Toulouse, France

    Jaques Blanc-Talon

  2. University of Auckland, Auckland, New Zealand

    Patrice Delmas

  3. Ghent University, Ghent, Belgium

    Wilfried Philips

  4. University of Antwerp, Wilrijk, Belgium

    Paul Scheunders

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Duong, D.Q. et al. (2023). A Deep Learning Approach to Segment High-Content Images of the E. coli Bacteria. In: Blanc-Talon, J., Delmas, P., Philips, W., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2023. Lecture Notes in Computer Science, vol 14124. Springer, Cham. https://doi.org/10.1007/978-3-031-45382-3_16

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  • DOI: https://doi.org/10.1007/978-3-031-45382-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45381-6

  • Online ISBN: 978-3-031-45382-3

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