@article {67226,
	title = {Deep learning enables fast and dense single-molecule localization with high accuracy},
	journal = {Nature Methods},
	volume = {18},
	year = {2021},
	month = {2021 Sep 03},
	abstract = {<p>Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but the need for activating only single isolated emitters limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE, a computational tool that can localize single emitters at high density in 3D with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 data-sets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to take live-cell SMLM data with reduced light exposure in just 3 seconds and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many labs to reduce imaging times and increase localization density in SMLM.Competing Interest StatementThe authors have declared no competing interest.</p>
},
	doi = {10.1101/2020.10.26.355164},
	url = {https://www.biorxiv.org/content/early/2020/10/26/2020.10.26.355164},
	author = {Speiser, Artur and M{\"u}ller, Lucas-Raphael and Matti, Ulf and Obara, Christopher J. and Legant, Wesley R. and Kreshuk, Anna and Macke, Jakob H. and Ries, Jonas and Turaga, Srinivas C.}
}
