Open AccessDeep learning enables fast and dense single-molecule localization with high accuracy
Author(s) -
Artur Speiser,
Lucas-Raphael Müller,
Philipp Hoess,
Ulf Matti,
Christopher J. Obara,
Wesley R. Legant,
Anna Kreshuk,
Jakob H. Macke,
Jonas Ries,
Srinivas C. Turaga
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-021-01236-x
Subject(s) - benchmark (surveying) , deep learning , computer science , context (archaeology) , artificial intelligence , margin (machine learning) , software , range (aeronautics) , superresolution , pattern recognition (psychology) , computer vision , image (mathematics) , materials science , machine learning , biology , paleontology , geodesy , composite material , programming language , geography
Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions 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 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM.