Deep learning

Pattern recognition (psychology)

Margin (machine learning)

Biology

Image (mathematics)

Geodesy

Machine learning

Superresolution

Paleontology

Benchmark (surveying)

Artificial intelligence

Composite material

Geography

Materials science

Programming language

Range (aeronautics)

Software

Context (archaeology)

Computer science

Computer vision

Nature Methods

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. DECODE uses deep learning for localizing single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. DECODE outperforms available methods and enables fast live-cell SMLM of dynamic processes.

Deep learning enables fast and dense single-molecule localization with high accuracy