CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations

Ji, Y., Cutiongco, M. F.A., Jensen, B. S. and Yuan, K. (2022) CP2Image: Generating High-Quality Single-Cell Images Using CellProfiler Representations. NeurIPS 2022 Workshop on Learning Meaningful Representations of Life (LMRL 2022), 12 Sept 2022.

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 or Workshop Item
• 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

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