@article{hardo_synthetic_2022,
	title = {Synthetic {Micrographs} of {Bacteria} ({SyMBac}) allows accurate segmentation of bacterial cells using deep neural networks},
	volume = {20},
	issn = {1741-7007},
	url = {https://doi.org/10.1186/s12915-022-01453-6},
	doi = {10.1186/s12915-022-01453-6},
	abstract = {Deep-learning–based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac).},
	number = {1},
	urldate = {2023-01-11},
	journal = {BMC Biology},
	author = {Hardo, Georgeos and Noka, Maximilian and Bakshi, Somenath},
	month = nov,
	year = {2022},
	keywords = {Bacterial cell imaging, Cell segmentation, Deep-learning, High-throughput imaging, Image analysis, Microfluidics, Synthetic images, Timelapse microscopy},
	pages = {263},
}