Ji, Y., Cutiongco, M. F.A., Jensen, B. S. and Yuan, K. (2023) CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations. In: Medical Imaging with Deep Learning (MIDL 2023), Nashville, TN, USA, 10-12 July 2023, pp. 1-12.
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Abstract
Single-cell high-throughput microscopy images contain key biological information underlying normal and pathological cellular processes. Image-based analysis and profiling are powerful and promising for extracting this information but are made difficult due to substantial complexity and heterogeneity in cellular phenotype. Hand-crafted methods and machine learning models are popular ways to extract cell image information. Representations extracted via machine learning models, which often exhibit good reconstruction performance, lack biological interpretability. Hand-crafted representations, on the contrary, have clear biological meanings and thus are interpretable. Whether these hand-crafted representations can also generate realistic images is not clear. In this paper, we propose a CellProfiler to image (CP2Image) model that can directly generate realistic cell images from CellProfiler representations. We also demonstrate most biological information encoded in the CellProfiler representations is well-preserved in the generating process. This is the first time hand-crafted representations be shown to have generative ability and provide researchers with an intuitive way for their further analysis.
| Item Type: | Conference Proceedings |
|---|---|
| Additional Information: | Yanni Ji acknowledges support from China Scholarship Council from the Ministry of Education of P.R. China. BSJ and KY acknowledge support from the Engineering and Physical Sciences Research Council (EPSRC, EP/R018634/1). KY also acknowledges support from Biotechnology and Biological Sciences Research Council (BBSRC, BB/V016067/1), European Union’s Horizon 2020 research and innovation programme project PANCAIM (101016851) and the Wellcome Trust (220977/Z/20/Z). |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Cutiongco, Marie Francene and Yuan, Dr Ke and Jensen, Dr Bjorn and Ji, Miss Yanni |
| Authors: | Ji, Y., Cutiongco, M. F.A., Jensen, B. S., and Yuan, K. |
| College/School: | College of Science and Engineering College of Science and Engineering > School of Computing Science |
| Copyright Holders: | Copyright © 2023 The Authors |
| First Published: | First published in Proceedings of Machine Learning Research 125:1–12 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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