Review on retinal blood vessel segmentation - an algorithmic perspective

Samuel, Pearl Mary; Veeramalai, Thanikaiselvan

doi:10.1504/ijbet.2020.110362

Cited by 9 publications

(5 citation statements)

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“…Networks of curvilinear objects such as keratin networks are ubiquitous ranging from cytoskeletal ﬁlaments at the micro- to nanoscale to blood vessels or neuronal networks at the tissue and organ level ( Özdemir and Reski, 2021 ). Filament detection systems have been developed for vascular ( Imran et al, 2019 ; Moccia et al, 2018 ; Samuel and Veeramalai, 2020 ) and neuronal networks ( Acciai et al, 2016 ; Magliaro et al, 2019 ; Quan et al, 2016 ). But these tools have not been optimized for subcellular structures and are specialized to detect branched, tree-like structures.…”

Section: Introductionmentioning

confidence: 99%

Quantitative mapping of keratin networks in 3D

Windoffer

Schwarz

Yoon

et al. 2022

eLife

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Mechanobiology requires precise quantitative information on processes taking place in specific 3D microenvironments. Connecting the abundance of microscopical, molecular, biochemical and cell mechanical data with defined topologies has turned out to be extremely difficult. Establishing such structural and functional 3D maps needed for biophysical modeling is a particular challenge for the cytoskeleton, which consists of long and interwoven filamentous polymers coordinating subcellular processes and interactions of cells with their environment. To date, useful tools are available for the segmentation and modeling of actin filaments and microtubules but comprehensive tools for the mapping of intermediate filament organization are still lacking. In this work, we describe a workflow to model and examine the complete 3D arrangement of the keratin intermediate filament cytoskeleton in canine, murine and human epithelial cells both, in vitro and in vivo. Numerical models are derived from confocal Airyscan high resolution 3D imaging of fluorescence-tagged keratin filaments. They are interrogated and annotated at different length scales using different modes of visualization including immersive virtual reality. In this way, information is provided on network organization at the subcellular level including mesh arrangement, density and isotropic configuration as well as details on filament morphology such as bundling, curvature and orientation. We show that the comparison of these parameters helps to identify, in quantitative terms, similarities and differences of keratin network organization in epithelial cell types defining subcellular domains, notably basal, apical, lateral and perinuclear systems. The described approach and the presented data are pivotal for generating mechanobiological models that can be experimentally tested.

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Section: Introductionmentioning

confidence: 99%

Quantitative mapping of keratin networks in 3D

Windoffer

Schwarz

Yoon

et al. 2022

eLife

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“…The retina is consisted of millions of nerve cells or photoreceptors that react to light [1]. The structure of the retinal blood vessels can help detect Diabetic Retinopathy (DR) [2]. So far, the detection of retinal blood vessels has been done manually by ophthalmologists through retinal images taken from the fundus camera.…”

Section: Introductionmentioning

confidence: 99%

BVU-Net: A U-Net Modification by VGG-Batch Normalization for Retinal Blood Vessel Segmentation

2022

IJIES

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The study proposes a BVU-Net architecture that combines the advantages of VGG and U-Net. The VGG architecture has a smaller kernel size which speeds up the training process. VGG bears some resemblance to the Encoder portion of U-Net. In this study, the BVU-Net encoder uses the VGG architecture with the addition of batch normalization. The addition of batch normalization aims to help simplify weight initialization so that the training process is faster and reduces the risk of overfitting, while the decoder section still uses the U-Net architecture. BVU-Net is expected to be able to overcome the weakness of U-Net architecture in retinal image blood vessel segmentation. The performance results produced by BVU-Net on the DRIVE dataset are 96.36% accuracy, 78.71 sensitivity, 98.1% specificity, F1-score 78.9, and IOU 0.65. The results of BVU-Net performance on the STARE dataset are accuracy of 96.39%, sensitivity of 79.35%, specificity of 98.52%, F1-score of 77.16%, and IoU of 0.63. Based on these results indicate that BVU-Net has a better performance on the DRIVE dataset in detecting retinal blood vessels than STARE. This can be seen from the sensitivity value of DRIVE which is higher than STARE. The BVU-Net IoU results on DRIVE and STARE are greater than 0.5 and the F1-score above 70%, indicating that BVU-Net is capable and balanced in detecting the intersection area between blood vessels and the background on retinal images.

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“…However, the computer-based detection approaches are preferred since they are more feasible and does not need skilled clinicians [2]. In fact, the development of screening systems and real-time classification of retinal diseases have become more helpful and effective [3].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Retinal Disease Detection Surveys

salih¹,

Kashmoola²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Retinal image analysis is crucial for The classification of retinal diseases such as "Age Related Macular Degeneration (AMD)", "Diabetic Retinopathy (DR)", "Retinoblastoma", "Macular Bunker", "Retinitis Pigmentosa", and "Retinal Detachment". The early detection of such diseases is important insofar as it contributes in mitigating future implications. Many approaches have been developed in the literature for auto-detecting of retinal landmarks and pathologies. The current revolution in deep learning techniques has opened the horizon for researchers in the field of ophthalmology. This paper is a comprehensive review of the deep learning techniques applied for the classification of retinal images, pathology, retinal landmarks, and disease classification. This review is based on indicators such as sensitivity, Area under ROC curve, specificity, F score, and accuracy.

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Review on retinal blood vessel segmentation - an algorithmic perspective

Cited by 9 publications

References 0 publications

Quantitative mapping of keratin networks in 3D

Quantitative mapping of keratin networks in 3D

BVU-Net: A U-Net Modification by VGG-Batch Normalization for Retinal Blood Vessel Segmentation

Deep Learning for Retinal Disease Detection Surveys

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