Title:
単一分子局在化顕微鏡法によって取得された回折限界画像からの高密度超解像度画像の再構築を改善する方法、装置、及びコンピュータプログラム
Document Type and Number:
Japanese Patent JP7249326
Kind Code:
B2
Abstract:
The invention relates to reconstructing a synthetic dense super-resolution image from at least one low-information-content image, for example from a sequence of diffraction-limited images acquired by single molecule localization microscopy. After having obtained such a sequence of diffraction-limited images, a sparse localization image is reconstructed from the obtained sequence of diffraction-limited images according to single molecule localization microscopy image processing. The reconstructed sparse localization image and/or a corresponding low-resolution wide-field image are input to an artificial neural network and a synthetic dense super-resolution image is obtained from the artificial neural network, the latter being trained with training data comprising triplets of sparse localization images, at least partially corresponding low-resolution wide-field images, and corresponding dense super-resolution images, as a function of a training objective function comparing dense super-resolution images and corresponding outputs of the artificial neural network.
Inventors:
Christoph Zimmer
Ouyang Way
Ouyang Way
Application Number:
JP2020504693A
Publication Date:
March 30, 2023
Filing Date:
July 26, 2018
Export Citation:
Assignee:
Ansty Pasteur
International Classes:
G06T3/40; G01N21/64; G02B21/00
Domestic Patent References:
| JP2017516992A |
Other References:
Siewert Hugelier, Johan J. de Rooi, Romain Bernex, Sam Duwe, Olivier Devos, Michel Sliwa, Peter Dedecker, Paul H. C. Eilers & Cyril Ruckebusch,Sparse deconvolution of high-density super-resolution images,Scientific Report,米国,Nature,2016年02月25日,pp.1-10,https://www.nature.com/articles/srep21413,DOI:10.1038/srep21413
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla,SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,IEEE Transactions on Pattern Analysis and Machine Intelligence,米国,IEEE,2017年12月01日,Volume: 39, Issue: 12,pp.2481-2495,https://ieeexplore.ieee.org/document/7803544
Anthony Barsic, Ginni Grover & Rafael Piestun,Three-dimensional super-resolution and localization of dense clusters of single molecules,Scientific Reports,米国,Nature,2014年06月23日,pp.1-8,https://www.researchgate.net/publication/263324933_Three-dimensional_super-resolution_and_localization_of_dense_clusters_of_single_molecules,DOI:10.1038/srep05388
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla,SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,IEEE Transactions on Pattern Analysis and Machine Intelligence,米国,IEEE,2017年12月01日,Volume: 39, Issue: 12,pp.2481-2495,https://ieeexplore.ieee.org/document/7803544
Anthony Barsic, Ginni Grover & Rafael Piestun,Three-dimensional super-resolution and localization of dense clusters of single molecules,Scientific Reports,米国,Nature,2014年06月23日,pp.1-8,https://www.researchgate.net/publication/263324933_Three-dimensional_super-resolution_and_localization_of_dense_clusters_of_single_molecules,DOI:10.1038/srep05388
Attorney, Agent or Firm:
Aoki Atsushi
Shinji Mihashi
Tomohiro Nanzan
Akira Kawai
Tsutomu Kono
Shinji Mihashi
Tomohiro Nanzan
Akira Kawai
Tsutomu Kono