Open AccessRapid image deconvolution and multiview fusion for optical microscopy
Author(s) -
Min Guo,
Yue Li,
Yijun Su,
Talley J. Lambert,
Damian Dalle Nogare,
Mark W. Moyle,
Leighton H. Duncan,
Richard Ikegami,
Anthony Santella,
Ivan Rey-Suarez,
Daniel Green,
Anastasia Beiriger,
Jiji Chen,
Harshad D. Vishwasrao,
Sundar Ganesan,
Victoria Prince,
Jennifer Waters,
Christina M. Annunziata,
Markus Hafner,
William A. Mohler,
Ajay Chitnis,
Arpita Upadhyaya,
Ted B. Usdin,
Zhirong Bao,
Daniel A. Colón-Ramos,
Patrick La Rivière,
Huafeng Liu,
Yicong Wu,
Hari Shroff
Publication year - 2020
Publication title -
nature biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 15.358
H-Index - 445
eISSN - 1546-1696
pISSN - 1087-0156
DOI - 10.1038/s41587-020-0560-x
Subject(s) - deconvolution , microscope , computer science , computer vision , artificial intelligence , microscopy , image resolution , image processing , light sheet fluorescence microscopy , graphics processing unit , projector , clearance , optics , computer graphics (images) , algorithm , image (mathematics) , physics , medicine , urology , scanning confocal electron microscopy , operating system
The contrast and resolution of images obtained with optical microscopes can be improved by deconvolution and computational fusion of multiple views of the same sample, but these methods are computationally expensive for large datasets. Here we describe theoretical and practical advances in algorithm and software design that result in image processing times that are tenfold to several thousand fold faster than with previous methods. First, we show that an 'unmatched back projector' accelerates deconvolution relative to the classic Richardson-Lucy algorithm by at least tenfold. Second, three-dimensional image-based registration with a graphics processing unit enhances processing speed 10- to 100-fold over CPU processing. Third, deep learning can provide further acceleration, particularly for deconvolution with spatially varying point spread functions. We illustrate our methods from the subcellular to millimeter spatial scale on diverse samples, including single cells, embryos and cleared tissue. Finally, we show performance enhancement on recently developed microscopes that have improved spatial resolution, including dual-view cleared-tissue light-sheet microscopes and reflective lattice light-sheet microscopes.