Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry

Holographic imaging flow cytometry (HIFC) can generate 2D quantitative phase maps of flowing cells in microchannels. When combined with convolutional neural networks (CNNs), HIFC could provide a promising stain-free approach for identifying target cells in complex cellular environments by leveraging the distinctive morphological and optical properties of different cell types. Here, we propose a lightweight CNN for HIFC image classification, tailored to distinguish ovarian cancer cells from surrounding non-neoplastic cell populations of the tumor microenvironment (TME). We show that the proposed CNN outperforms commonly used models, i.e., Resnet and VGG, with a computational cost lower than Mobilenet, the benchmark for efficiency and accuracy. Our approach could streamline ovarian cancer diagnostics and improve understanding of the TME, ultimately aiding the development of personalized treatments.

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Publication Details

Journal
Biomedical Optics Express
Published
2024-12-13
DOI
https://doi.org/10.1364/boe.545251
Citations
2
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
1.32
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article

Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry

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2 citations
Biomedical Optics Express
Cell Image Analysis Techniques
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article

Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry

Giuseppe Gasparre, Daniele Pirone, Giusy Giugliano, Francesca Reggiani, Lisa Miccio, Pietro Ferraro, Ivana Kurelac, Pasquale Memmolo, Vittorio Bianco, Beatrice Cavina, Francesca Nanetti, Michela Schiavo
article en
2 citations

Abstract

Holographic imaging flow cytometry (HIFC) can generate 2D quantitative phase maps of flowing cells in microchannels. When combined with convolutional neural networks (CNNs), HIFC could provide a promising stain-free approach for identifying target cells in complex cellular environments by leveraging the distinctive morphological and optical properties of different cell types. Here, we propose a lightweight CNN for HIFC image classification, tailored to distinguish ovarian cancer cells from surrounding non-neoplastic cell populations of the tumor microenvironment (TME). We show that the proposed CNN outperforms commonly used models, i.e., Resnet and VGG, with a computational cost lower than Mobilenet, the benchmark for efficiency and accuracy. Our approach could streamline ovarian cancer diagnostics and improve understanding of the TME, ultimately aiding the development of personalized treatments.

Biomedical Optics ExpressVol. 16(11)
University of Campania "Luigi Vanvitelli" (IT), Azienda Sanitaria Unità Locale di Reggio Emilia (IT), Institute of Applied Science and Intelligent Systems (IT), Federico II University Hospital (IT), Telethon Institute Of Genetics And Medicine (IT), University of Naples Federico II (IT), University of Bologna (IT)
Reduced inequalities
Openalex Percentile: Top 14%
Cell Image Analysis Techniques
1.32
DOIOpen Access PDFOpenAlex
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