nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer

Reka Hollandi, Abel Szkalisity, Timea Toth, Ervin Tasnadi, Csaba Molnar, Botond Mathe, Istvan Grexa, Jozsef Molnar, Arpad Balind, Mate Gorbe, Maria Kovacs, Ede Migh, Allen Goodman, Tamas Balassa, Krisztian Koos, Wenyu Wang, Juan Carlos Caicedo, Norbert Bara, Ferenc Kovacs, Lassi PaavolainenTivadar Danka, Andras Kriston, Anne Elizabeth Carpenter, Kevin Smith, Peter Horvath

Research output: Contribution to journalArticle

Abstract

Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is that during training nucleAIzer automatically adapts its nucleus-style model to unseen and unlabeled data using image style transfer to automatically generate augmented training samples. This allows the model to recognize nuclei in new and different experiments efficiently without requiring expert annotations, making deep learning for nucleus segmentation fairly simple and labor free for most biological light microscopy experiments. It can also be used online, integrated into CellProfiler and freely downloaded at www.nucleaizer.org. A record of this paper's transparent peer review process is included in the Supplemental Information. Microscopy image analysis of single cells can be challenging but also eased and improved. We developed a deep learning method to segment cell nuclei. Our strategy is adapting to unexpected circumstances automatically by synthesizing artificial microscopy images in such a domain as training samples.

Original languageEnglish
Pages (from-to)453-458.e6
JournalCell Systems
Volume10
Issue number5
DOIs
Publication statusPublished - May 20 2020

Keywords

  • cellular analysis
  • deep learning
  • high-content screening
  • microscopy image analysis
  • segmentation

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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  • Cite this

    Hollandi, R., Szkalisity, A., Toth, T., Tasnadi, E., Molnar, C., Mathe, B., Grexa, I., Molnar, J., Balind, A., Gorbe, M., Kovacs, M., Migh, E., Goodman, A., Balassa, T., Koos, K., Wang, W., Caicedo, J. C., Bara, N., Kovacs, F., ... Horvath, P. (2020). nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer. Cell Systems, 10(5), 453-458.e6. https://doi.org/10.1016/j.cels.2020.04.003