Imaging error compensation method for through‐focus scanning optical microscopy images based on deep learning

2021 ◽  
Author(s):  
Qu Yufu ◽  
Ren Jiajun ◽  
Peng Renju ◽  
Wang Qianyi
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Error Compensation ◽  
Compensation Method ◽  
Scanning Optical Microscopy ◽  
Microscopy Images

Deep learning discrimination of quartz and resin in optical microscopy images of minerals

2019 ◽  
Vol 138 ◽  
pp. 79-85 ◽  
Author(s):  
Julio César Álvarez Iglesias ◽  
Richard Bryan Magalhaes Santos ◽  
Sidnei Paciornik
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Microscopy Images

Influence of secondary tip shape on illumination-mode near-field scanning optical microscopy images

1999 ◽  
Vol 16 (8) ◽  
pp. 1936 ◽  
Author(s):  
Lee J. Richter ◽  
Claire E. Jordan ◽  
Richard R. Cavanagh ◽  
Garnett W. Bryant ◽  
Ansheng Liu ◽  
...  
Keyword(s):  
Optical Microscopy ◽  
Near Field ◽  
Scanning Optical Microscopy ◽  
Near Field Scanning ◽  
Microscopy Images ◽  
Illumination Mode

scholarly journals Deep Learning Segmentation of Optical Microscopy Images Improves 3-D Neuron Reconstruction

2017 ◽  
Vol 36 (7) ◽  
pp. 1533-1541 ◽  
Author(s):  
Rongjian Li ◽  
Tao Zeng ◽  
Hanchuan Peng ◽  
Shuiwang Ji
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Neuron Reconstruction ◽  
Microscopy Images

scholarly journals Unsupervised content-preserving transformation for optical microscopy

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Xinyang Li ◽  
Guoxun Zhang ◽  
Hui Qiao ◽  
Feng Bao ◽  
Yue Deng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Microscopy ◽  
Training Data ◽  
Fluorescence Labeling ◽  
Imaging Data ◽  
Image Transformation ◽  
General Applicability ◽  
Data Annotation ◽  
Biomedical Image ◽  
Wide Range

AbstractThe development of deep learning and open access to a substantial collection of imaging data together provide a potential solution for computational image transformation, which is gradually changing the landscape of optical imaging and biomedical research. However, current implementations of deep learning usually operate in a supervised manner, and their reliance on laborious and error-prone data annotation procedures remains a barrier to more general applicability. Here, we propose an unsupervised image transformation to facilitate the utilization of deep learning for optical microscopy, even in some cases in which supervised models cannot be applied. Through the introduction of a saliency constraint, the unsupervised model, named Unsupervised content-preserving Transformation for Optical Microscopy (UTOM), can learn the mapping between two image domains without requiring paired training data while avoiding distortions of the image content. UTOM shows promising performance in a wide range of biomedical image transformation tasks, including in silico histological staining, fluorescence image restoration, and virtual fluorescence labeling. Quantitative evaluations reveal that UTOM achieves stable and high-fidelity image transformations across different imaging conditions and modalities. We anticipate that our framework will encourage a paradigm shift in training neural networks and enable more applications of artificial intelligence in biomedical imaging.

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