University of Leicester
Browse

CLANet: A Comprehensive Framework for Cross-Batch Cell Line Identification Using Brightfield Images

Download (3.95 MB)
Version 2 2024-10-09, 10:54
Version 1 2024-02-26, 12:46
journal contribution
posted on 2024-10-09, 10:54 authored by L Tong, A Corrigan, NR Kumar, K Hallbrook, J Orme, Y Wang, Huiyu Zhou

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL’s feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.

Funding

University of Leicester GTA studentship (GTA 2020)

China Scholarship Council

AstraZeneca – University Leicester collaboration agreement(CR-019972)

History

Author affiliation

College of Science & Engineering/Comp' & Math' Sciences

Version

  • VoR (Version of Record)

Published in

Medical Image Analysis

Volume

94

Publisher

Elsevier

issn

1361-8423

Copyright date

2024

Available date

2024-10-09

Language

en

Deposited by

Professor Huiyu Zhou

Deposit date

2024-02-25

Data Access Statement

Our experimental data and code will be published via https://github.com/LeiTong02/CLANet .

Usage metrics

    University of Leicester Publications

    Categories

    No categories selected

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC