Deriving Visual Cues from Deep Learning to Achieve Subpixel Cell Segmentation in Adaptive Optics Retinal Images

Solar cells may possess defects during the manufacturing process in photovoltaic (PV) industries. To precisely evaluate the effectiveness of solar PV modules, manufacturing defects are required to be identified. Conventional defect inspection in industries mainly depends on manual defect inspection by highly skilled inspectors, which may still give inconsistent, subjective identification results. In order to automatize the visual defect inspection process, an automatic cell segmentation technique and a convolutional neural network (CNN)-based defect detection system with pseudo-colorization of defects is designed in this paper. High-resolution Electroluminescence (EL) images of single-crystalline silicon (sc-Si) solar PV modules are used in our study for the detection of defects and their quality inspection. Firstly, an automatic cell segmentation methodology is developed to extract cells from an EL image. Secondly, defect detection can be actualized by CNN-based defect detector and can be visualized with pseudo-colors. We used contour tracing to accurately localize the panel region and a probabilistic Hough transform to identify gridlines and busbars on the extracted panel region for cell segmentation. A cell-based defect identification system was developed using state-of-the-art deep learning in CNNs. The detected defects are imposed with pseudo-colors for enhancing defect visualization using K-means clustering. Our automatic cell segmentation methodology can segment cells from an EL image in about 2.71 s. The average segmentation errors along the x-direction and y-direction are only 1.6 pixels and 1.4 pixels, respectively. The defect detection approach on segmented cells achieves 99.8% accuracy. Along with defect detection, the defect regions on a cell are furnished with pseudo-colors to enhance the visualization.

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Understanding the changes of cone reflectance in adaptive optics flood illumination retinal images over three years

Biomedical Optics Express ◽

10.1364/boe.7.002807 ◽

2016 ◽

Vol 7 (7) ◽

pp. 2807 ◽

Cited By ~ 4

Author(s):

Letizia Mariotti ◽

Nicholas Devaney ◽

Giuseppe Lombardo ◽

Marco Lombardo

Keyword(s):

Adaptive Optics ◽

Retinal Images

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Marginal blind deconvolution of adaptive optics retinal images

Optics Express ◽

10.1364/oe.19.023227 ◽

2011 ◽

Vol 19 (23) ◽

pp. 23227 ◽

Cited By ~ 24

Author(s):

L. Blanco ◽

L. M. Mugnier

Keyword(s):

Adaptive Optics ◽

Blind Deconvolution ◽

Retinal Images

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MiSiC, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

eLife ◽

10.7554/elife.65151 ◽

2021 ◽

Vol 10 ◽

Author(s):

Swapnesh Panigrahi ◽

Dorothée Murat ◽

Antoine Le Gall ◽

Eugénie Martineau ◽

Kelly Goldlust ◽

...

Keyword(s):

Deep Learning ◽

Bacterial Communities ◽

Cell Biology ◽

Imaging Modality ◽

Interaction Model ◽

Cell Segmentation ◽

Bacterial Cells ◽

Interspecies Interactions ◽

Prey Interaction ◽

Simple Implementation

Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.

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Improving Efficiency in Separating Blood Vessels from Retinal Images with Deep Learning Techniques

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1457.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 3637-3640

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Macular Degeneration ◽

Blood Vessels ◽

Age Related Macular Degeneration ◽

Retinal Images ◽

Retinal Vessels ◽

Age Related ◽

Learning Techniques ◽

Retinal Pathologies

Retinal vessels ID means to isolate the distinctive retinal configuration issues, either wide or restricted from fundus picture foundation, for example, optic circle, macula, and unusual sores. Retinal vessels recognizable proof investigations are drawing in increasingly more consideration today because of pivotal data contained in structure which is helpful for the identification and analysis of an assortment of retinal pathologies included yet not restricted to: Diabetic Retinopathy (DR), glaucoma, hypertension, and Age-related Macular Degeneration (AMD). With the advancement of right around two decades, the inventive methodologies applying PC supported systems for portioning retinal vessels winding up increasingly significant and coming nearer. Various kinds of retinal vessels segmentation strategies discussed by using Deep Learning methods. At that point, the pre-processing activities and the best in class strategies for retinal vessels distinguishing proof are presented.

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