Quantitative evaluation of software packages for single-molecule localization microscopy

By Daniel Sage, H. Kirshner, T. Pengo, N. Stuurman, Junhong Min, Suliana Manley, Michael A. Unser. Nature Methods, 2015

400 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Biophysics, Structural Biology

View Paper: https://www.semanticscholar.org/paper/b5b3ad68dcab65ab9782148d8190a110202ef0b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Klaus Hermann Maier-Hein
2. Ben Glocker
3. Fabian Isensee
4. B. van Ginneken

In Biophysics

1. Erik H. W. Meijering
2. Michael Alexander Riegler
3. Klaus Hermann Maier-Hein
4. J. Sáez-Rodríguez
5. Ben Glocker
6. Sotirios A. Tsaftaris
7. Mauricio Reyes
8. Gaël Varoquaux

In Structural Biology

1. Erik H. W. Meijering

Citations in the corpus

• Deep learning massively accelerates super-resolution localization microscopy (2018, methodology): “…nuclear pores, or mitochondria) are obtained using standard PALM imaging, that is, by acquiring long diffraction-limited image sequences (e.g., K ~ 10 4 − 10 5 , N ~ 10 5 − 10 7 ) and processing them with standard localization software 19 , resulting in highly sampled (dense) PALM images.”
• Metrics Reloaded: Recommendations for image analysis validation (2022, background): “These challenges are often published in prestigious journals [22, 73, 87] and receive tremendous attention from both the scientific community and industry.”