@article{MTMT:31334623,
	title = {nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer},
	url = {https://m2.mtmt.hu/api/publication/31334623},
	author = {Hollandi, Réka and Szkalisity, Ábel and Tóth, Tímea and Tasnádi, Ervin Áron and Molnár, Csaba and Mathe, Botond and Grexa, István and Molnár, József and Bálind, Árpád and Gorbe, Mate and Kovács, Mária and Migh, Ede and Goodman, Allen and Balassa, Tamás and Koós, Krisztián and Wang, Wenyu and Caicedo, Juan Carlos and Bara, Norbert and Kovács, Ferenc and Paavolainen, Lassi and Danka, Tivadar and Kriston, András and Carpenter, Anne Elizabeth and Smith, Kevin and Horváth, Péter},
	doi = {10.1016/j.cels.2020.04.003},
	journal-iso = {CELL SYST},
	journal = {CELL SYSTEMS},
	volume = {10},
	unique-id = {31334623},
	issn = {2405-4712},
	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.},
	year = {2020},
	eissn = {2405-4720},
	pages = {453-458},
	orcid-numbers = {Molnár, Csaba/0000-0002-6124-1209; Molnár, József/0000-0002-9185-9376}
}