scholarly journals Image analysis and glare sensitivity in human age-related cataracts

2003 ◽  
Vol 86 (3) ◽  
pp. 157-172 ◽  
Author(s):  
Mark A. Babizhayev ◽  
Anatoly I. Deyev ◽  
Valentina N. Yermakova ◽  
Nina G. Davydova ◽  
Natarya I. Kurysheva ◽  
...  
Keyword(s):  
Image Analysis ◽  
Age Related ◽  
Glare Sensitivity

scholarly journals The Impact of Aging on Macroautophagy in the Pre-ovulatory Mouse Oocyte

2021 ◽  
Vol 9 ◽  
Author(s):  
Alexandra E. Peters ◽  
Shandelle J. Caban ◽  
Eileen A. McLaughlin ◽  
Shaun D. Roman ◽  
Elizabeth G. Bromfield ◽  
...  
Keyword(s):  
Image Analysis ◽  
Degradation Mechanism ◽  
Oocyte Quality ◽  
Poor Quality ◽  
Depth Image ◽  
Age Related ◽  
Gene And Protein Expression ◽  
Quantitative Image ◽  
Autophagy Pathway ◽  
The Impact

Accompanying the precipitous age-related decline in human female fertility is an increase in the proportion of poor-quality oocytes within the ovary. The macroautophagy pathway, an essential protein degradation mechanism responsible for maintaining cell health, has not yet been thoroughly investigated in this phenomenon. The aim of this study was to characterize the macroautophagy pathway in an established mouse model of oocyte aging using in-depth image analysis-based methods and to determine mechanisms that account for the observed changes. Three autophagy pathway markers were selected for assessment of gene and protein expression in this model: Beclin 1; an initiator of autophagosome formation, Microtubule-associated protein 1 light chain 3B; a constituent of the autophagosome membrane, and lysosomal-associated membrane protein 1; a constituent of the lysosome membrane. Through quantitative image analysis of immunolabeled oocytes, this study revealed impairment of the macroautophagy pathway in the aged oocyte with an attenuation of both autophagosome and lysosome number. Additionally, an accumulation of amphisomes greater than 10 μm2 in area were observed in aging oocytes, and this accumulation was mimicked in oocytes treated with lysosomal inhibitor chloroquine. Overall, these findings implicate lysosomal dysfunction as a prominent mechanism by which these age-related changes may occur and highlight the importance of macroautophagy in maintaining mouse pre-ovulatory oocyte quality. This provides a basis for further investigation of dysfunctional autophagy in poor oocyte quality and for the development of therapeutic or preventative strategies to aid in the maintenance of pre-ovulatory oocyte health.

Image analysis of skin color heterogeneity focusing on skin chromophores and the age-related changes in facial skin

2014 ◽  
Vol 21 (2) ◽  
pp. 175-183 ◽  
Author(s):  
Kumiko Kikuchi ◽  
Yuji Masuda ◽  
Toyonobu Yamashita ◽  
Eriko Kawai ◽  
Tetsuji Hirao
Keyword(s):  
Image Analysis ◽  
Skin Color ◽  
Facial Skin ◽  
Age Related ◽  
Age Related Changes

Retinal Image Analysis: A Review

2012 ◽  
pp. 265-269
Author(s):  
S. R. Nirmala ◽  
Malaya Kumar Nath ◽  
Samarendra Dandapat
Keyword(s):  
Image Analysis ◽  
Retinal Image ◽  
Screening Tool ◽  
Review Paper ◽  
Age Related Macular Degeneration ◽  
Hypertensive Retinopathy ◽  
Human Errors ◽  
Remote Areas ◽  
Age Related ◽  
The Relationship

Images of the eye ground or retina not only provide an insight to important parts of the visual system but also reflect the general state of health of the entire human body. Automated retina image analysis is becoming an important screening tool for early detection of certain risks and diseases like diabetic retinopathy, hypertensive retinopathy, age related macular degeneration, glaucoma etc. This can in turn be used to reduce human errors or to provide services to remote areas. In this review paper, we discuss some of the current techniques used to automatically detect the important clinical features of retinal image, such as the blood vessels, optic disc and macula. The quantitative analysis and measurements of these features can be used to better understand the relationship between various diseases and the retinal features.

scholarly journals Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study

2020 ◽  
Vol 2020 ◽  
pp. 1-7 ◽  
Author(s):  
Ehsan Vaghefi ◽  
Sophie Hill ◽  
Hannah M. Kersten ◽  
David Squirrell
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Diagnostic Accuracy ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Fundus Photography ◽  
Multiple Modalities ◽  
Age Related ◽  
Dry Amd ◽  
Fundus Photographs

Background and Objective. To determine if using a multi-input deep learning approach in the image analysis of optical coherence tomography (OCT), OCT angiography (OCT-A), and colour fundus photographs increases the accuracy of a CNN to diagnose intermediate dry age-related macular degeneration (AMD). Patients and Methods. Seventy-five participants were recruited and divided into three cohorts: young healthy (YH), old healthy (OH), and patients with intermediate dry AMD. Colour fundus photography, OCT, and OCT-A scans were performed. The convolutional neural network (CNN) was trained on multiple image modalities at the same time. Results. The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions. Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.

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