Fast and robust multiplane single-molecule localization microscopy using a deep neural network

Toshimitsu Aritake*, Hideitsu Hino, Shigeyuki Namiki, Daisuke Asanuma, Kenzo Hirose, Noboru Murata

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Single-molecule localization microscopy is a widely used technique in biological research for measuring the nanostructures of samples smaller than the diffraction limit. This study uses multifocal plane microscopy and addresses the three-dimensional (3D) single-molecule localization problem, where lateral and axial locations of molecules are estimated. However, when multifocal plane microscopy is used, the estimation accuracy of 3D localization is easily deteriorated by the small lateral drifts of camera positions. A 3D molecule localization problem was presented along with the lateral drift estimation as a compressed sensing problem. A deep neural network (DNN) was applied to solve this problem accurately and efficiently. The results show that the proposed method is robust to lateral drift and achieves an accuracy of 20 nm laterally and 50 nm axially without an explicit drift correction.

Original languageEnglish
Pages (from-to)279-289
Number of pages11
Publication statusPublished - 2021 Sept 3


  • 3D single-molecule localization microscopy
  • Convolutional neural network
  • Lateral drift
  • Multi-focal plane microscopy

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


Dive into the research topics of 'Fast and robust multiplane single-molecule localization microscopy using a deep neural network'. Together they form a unique fingerprint.

Cite this