期刊
CELL SYSTEMS
卷 10, 期 5, 页码 453-+出版社
CELL PRESS
DOI: 10.1016/j.cels.2020.04.003
关键词
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资金
- LENDULET-BIOMAG grant [2018-342]
- European Regional Development Funds [GINOP-2.3.2-15-201600006, GINOP-2.3.2-15-2016-00026, GINOP-2.3.2-15-2016-00037]
- Academy of Finland [310552]
- National Institute of General Medical Sciences of the National Institutes of Health under MIRA [R35 GM122547]
- Swedish Research Council (Vetenskapsr_adet) [2017040609_3]
- Academy of Finland (AKA) [310552, 310552] Funding Source: Academy of Finland (AKA)
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.
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