scholarly journals Intensity normalization of two-photon microscopy images for liver fibrosis analysis

2011 ◽  
Author(s):  
Vijay Raj Singh ◽  
Jagath C. Rajapakse ◽  
Hanry Yu ◽  
Peter T. C. So
Keyword(s):  
Liver Fibrosis ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Intensity Normalization ◽  
Microscopy Images

scholarly journals Fibro-C-Index: comprehensive, morphology-based quantification of liver fibrosis using second harmonic generation and two-photon microscopy

2009 ◽  
Vol 14 (4) ◽  
pp. 044013 ◽  
Author(s):  
Dean C. S. Tai ◽  
Nancy Tan ◽  
Shuoyu Xu ◽  
Chiang Huen Kang ◽  
Ser Mien Chia ◽  
...  
Keyword(s):  
Liver Fibrosis ◽  
Second Harmonic Generation ◽  
Harmonic Generation ◽  
Second Harmonic ◽  
Two Photon Microscopy ◽  
Two Photon

scholarly journals Categorization of two-photon microscopy images of human cartilage into states of osteoarthritis

2013 ◽  
Vol 21 (8) ◽  
pp. 1074-1082 ◽  
Author(s):  
T. Bergmann ◽  
U. Maeder ◽  
M. Fiebich ◽  
M. Dickob ◽  
T.W. Nattkemper ◽  
...  
Keyword(s):  
Human Cartilage ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Microscopy Images

Automatic Identification of the Activity Area in Brain with Deep Neural Networks

2014 ◽  
Vol 556-562 ◽  
pp. 4941-4944
Author(s):  
Li Xiang Shi ◽  
Li Peng ◽  
Lu Lu Yue ◽  
Zhi Xing Huang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Automatic Segmentation ◽  
Threshold Value ◽  
Automatic Identification ◽  
Post Processing ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Central Pixel ◽  
Microscopy Images

We use deep max-pooling convolutional neural networks to address a problem of neuroanatomy, namely, the automatic segmentation of cerebral cortex structures of laboratory rat depicted in stacks of Two-photon microscopy images and detect the change areas when stimulation occurs. We classify each pixel in the image by training a CNN network, using a square window to predict the probability of the central pixel for each class. After classification, we perform the post-processing on the output produced by CNN. At last, we depict the areas that we interested through a threshold value.

Dendritic spine shape classification from two-photon microscopy images

2015 ◽  
Author(s):  
Muhammad Usman Ghani ◽  
Sumeyra Demir Kanik ◽  
Ali Ozgur Argunsah ◽  
Tolga Tasdizen ◽  
Devrim Unay ◽  
...  
Keyword(s):  
Dendritic Spine ◽  
Shape Classification ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
Spine Shape ◽  
Microscopy Images

scholarly journals Robust blind spectral unmixing for fluorescence microscopy using unsupervised learning

2019 ◽  
Author(s):  
Tristan D. McRae ◽  
David Oleksyn ◽  
Jim Miller ◽  
Yu-Rong Gao
Keyword(s):  
Fluorescence Microscopy ◽  
Emission Spectra ◽  
Principal Component ◽  
Spectral Unmixing ◽  
Spectral Signatures ◽  
Two Photon Microscopy ◽  
Two Photon ◽  
False Positive Detection ◽  
Linear Unmixing ◽  
Microscopy Images

AbstractDue to the overlapping emission spectra of fluorophores, fluorescence microscopy images often have bleed-through problems, leading to a false positive detection. This problem is almost unavoidable when the samples are labeled with three or more fluorophores, and the situation is complicated even further when imaged under a multiphoton microscope. Several methods have been developed and commonly used by biologists for fluorescence microscopy spectral unmixing, such as linear unmixing, non-negative matrix factorization, deconvolution, and principal component analysis. However, they either require pre-knowledge of emission spectra or restrict the number of fluorophores to be the same as detection channels, which highly limits the real-world applications of those spectral unmixing methods. In this paper, we developed a robust and flexible spectral unmixing method: Learning Unsupervised Means of Spectra (LUMoS), which uses an unsupervised machine learning clustering method to learn individual fluorophores’ spectral signatures from mixed images, and blindly separate channels without restrictions on the number of fluorophores that can be imaged. This method highly expands the hardware capability of two-photon microscopy to simultaneously image more fluorophores than is possible with instrumentation alone. Experimental and simulated results demonstrated the robustness of LUMoS in multi-channel separations of two-photon microscopy images. We also extended the application of this method to background/autofluorescence removal and colocalization analysis. Lastly, we integrated this tool into ImageJ to offer an easy to use spectral unmixing tool for fluorescence imaging. LUMoS allows us to gain a higher spectral resolution and obtain a cleaner image without the need to upgrade the imaging hardware capabilities.

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