Deep learning multi-shot 3D localization microscopy using hybrid optical–electronic computing
Abstract
Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical–electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 13, 2021
- Source ID
- 10.1364/ol.441743
Entities
People
- Gordon Wetzstein
- Hayato Ikoma
- Michael Broxton
- Takamasa Kudo
- Yifan Peng
Organizations
- Army Research Office
- Defense Advanced Research Projects Agency
- National Science Foundation
- Olympus Corporation
- Stanford University