Investigating the correlation between white matter and microvasculature changes in aging using large scale optical coherence tomography and confocal fluorescence imaging combined with tissue sectioning

2015 ◽  
Author(s):  
Alexandre Castonguay ◽  
Pramod K. Avti ◽  
Mohammad Moeini ◽  
Philippe Pouliot ◽  
Maryam S. Tabatabaei ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
White Matter ◽  
Fluorescence Imaging ◽  
Large Scale ◽  
Optical Coherence ◽  
Confocal Fluorescence ◽  
Confocal Fluorescence Imaging

Confocal Fluorescence Imaging of Photosensitised DNA Denaturation in Cell Nuclei

2005 ◽  
Author(s):  
Tytus Bernas ◽  
Elikplimi K. Asem ◽  
J. Paul Robinson ◽  
Peter R. Cook ◽  
Jurek W. Dobrucki
Keyword(s):  
Fluorescence Imaging ◽  
Dna Denaturation ◽  
Cell Nuclei ◽  
Confocal Fluorescence ◽  
Confocal Fluorescence Imaging

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Pore selectivity and electron transfers in HZSM-5 single crystals: a Raman microspectroscopy mapping and confocal fluorescence imaging combined study

2020 ◽  
Vol 22 (22) ◽  
pp. 12745-12756
Author(s):  
A. Moissette ◽  
M. Hureau ◽  
M. Moreau ◽  
J. P. Cornard
Keyword(s):  
Single Crystals ◽  
Fluorescence Imaging ◽  
Single Particle ◽  
Raman Microspectroscopy ◽  
Single Particle Level ◽  
Confocal Fluorescence ◽  
Particle Level ◽  
Confocal Fluorescence Imaging ◽  
Electron Transfers

Electron transfers at the single particle level in HZSM-5 zeolite are followed by combining Raman microspectroscopy mapping and confocal fluorescence imaging. The effects of pore accessibility and guest diffusion on reactivity are investigated.

scholarly journals Validating Intravascular Imaging with Serial Optical Coherence Tomography and Confocal Fluorescence Microscopy

2016 ◽  
Vol 17 (12) ◽  
pp. 2110 ◽  
Author(s):  
Pier-Luc Tardif ◽  
Marie-Jeanne Bertrand ◽  
Maxime Abran ◽  
Alexandre Castonguay ◽  
Joël Lefebvre ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Fluorescence Microscopy ◽  
Confocal Fluorescence Microscopy ◽  
Intravascular Imaging ◽  
Optical Coherence ◽  
Confocal Fluorescence

scholarly journals Retinal Damage in Multiple Sclerosis Disease Subtypes Measured by High-Resolution Optical Coherence Tomography

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Timm Oberwahrenbrock ◽  
Sven Schippling ◽  
Marius Ringelstein ◽  
Falko Kaufhold ◽  
Hanna Zimmermann ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Optic Neuritis ◽  
Large Scale ◽  
Retinal Nerve Fibre Layer ◽  
Spectral Domain ◽  
Healthy Controls ◽  
Optical Coherence ◽  
Cross Sectional ◽  
Spectral Domain Oct ◽  
Rnfl Thickness

Background.Optical coherence tomography (OCT) has facilitated characterisation of retinal alterations in MS patients. Only scarce and in part conflicting data exists on different MS subtypes.Objective.To analyse patterns of retinal changes in different subtypes of MS with latest spectral-domain technology.Methods.In a three-centre cross-sectional study 414 MS patients and 94 healthy controls underwent spectral-domain OCT examination.Results.Eyes of MS patients without a previous optic neuritis showed a significant reduction of both retinal nerve fibre layer (RNFL) thickness and total macular volume (TMV) compared to healthy controls independent of the MS subtype (P<0.001for all subtypes). RNFL thickness was lower in secondary progressive MS (SPMS) eyes compared to relapsing-remitting MS (RRMS) eyes (P=0.007), and TMV was reduced in SPMS and primary progressive MS (PPMS) eyes compared to RRMS eyes (SPMS:P=0.039, PPMS:P=0.005). Independent of the subtype a more pronounced RNFL thinning and TMV reduction were found in eyes with a previous optic neuritis compared to unaffected eyes.Conclusion.Analysis of this large-scale cross-sectional dataset of MS patients studied with spectral-domain OCT confirmed and allows to generalize previous findings. Furthermore it carves out distinct patterns in different MS subtypes.

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