MONET -- Virtual Cell Painting of Brightfield Images and Time Lapses Using Reference Consistent Diffusion

• URL: http://arxiv.org/abs/2512.11928v1
• Date: Fri, 12 Dec 2025 01:01:34 GMT
• Title: MONET -- Virtual Cell Painting of Brightfield Images and Time Lapses Using Reference Consistent Diffusion
• Authors: Alexander Peysakhovich, William Berman, Joseph Rufo, Felix Wong, Maxwell Z. Wilson,
• Abstract summary: Cell painting is a popular technique for creating human-interpretable images of cell morphology.<n>There are two major issues with cell paint: it is labor-intensive and it requires chemical fixation.<n>We train a diffusion model on a large dataset to predict cell paint channels from brightfield images.
• Score: 37.62160903348546
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cell painting is a popular technique for creating human-interpretable, high-contrast images of cell morphology. There are two major issues with cell paint: (1) it is labor-intensive and (2) it requires chemical fixation, making the study of cell dynamics impossible. We train a diffusion model (Morphological Observation Neural Enhancement Tool, or MONET) on a large dataset to predict cell paint channels from brightfield images. We show that model quality improves with scale. The model uses a consistency architecture to generate time-lapse videos, despite the impossibility of obtaining cell paint video training data. In addition, we show that this architecture enables a form of in-context learning, allowing the model to partially transfer to out-of-distribution cell lines and imaging protocols. Virtual cell painting is not intended to replace physical cell painting completely, but to act as a complementary tool enabling novel workflows in biological research.

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