FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225295159> ?p ?o ?g. }

• W4225295159 endingPage "1536" @default.
• W4225295159 startingPage "1536" @default.
• W4225295159 abstract "Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis." @default.
• W4225295159 created "2022-05-05" @default.
• W4225295159 creator A5002524886 @default.
• W4225295159 creator A5050153883 @default.
• W4225295159 creator A5080344855 @default.
• W4225295159 creator A5083173630 @default.
• W4225295159 date "2022-05-03" @default.
• W4225295159 modified "2023-10-09" @default.
• W4225295159 title "Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis" @default.
• W4225295159 cites W2030935378 @default.
• W4225295159 cites W2045227075 @default.
• W4225295159 cites W2090802810 @default.
• W4225295159 cites W2100756624 @default.
• W4225295159 cites W2138786322 @default.
• W4225295159 cites W2145305441 @default.
• W4225295159 cites W2150769593 @default.
• W4225295159 cites W2411244326 @default.
• W4225295159 cites W2508948606 @default.
• W4225295159 cites W2587499148 @default.
• W4225295159 cites W2606663706 @default.
• W4225295159 cites W2618884931 @default.
• W4225295159 cites W2734595418 @default.
• W4225295159 cites W2792110043 @default.
• W4225295159 cites W2800012666 @default.
• W4225295159 cites W2801013643 @default.
• W4225295159 cites W2802388893 @default.
• W4225295159 cites W2809144587 @default.
• W4225295159 cites W2893691907 @default.
• W4225295159 cites W2898910301 @default.
• W4225295159 cites W2902329146 @default.
• W4225295159 cites W2911558038 @default.
• W4225295159 cites W2912395520 @default.
• W4225295159 cites W2916475144 @default.
• W4225295159 cites W2941942490 @default.
• W4225295159 cites W2963881378 @default.
• W4225295159 cites W2967002091 @default.
• W4225295159 cites W2973003726 @default.
• W4225295159 cites W2976384268 @default.
• W4225295159 cites W2977037385 @default.
• W4225295159 cites W2986274801 @default.
• W4225295159 cites W2991196029 @default.
• W4225295159 cites W2991564428 @default.
• W4225295159 cites W2999301272 @default.
• W4225295159 cites W3006026428 @default.
• W4225295159 cites W3009736969 @default.
• W4225295159 cites W3010373058 @default.
• W4225295159 cites W3025177399 @default.
• W4225295159 cites W3030832441 @default.
• W4225295159 cites W3035941285 @default.
• W4225295159 cites W3035974373 @default.
• W4225295159 cites W3043116777 @default.
• W4225295159 cites W3085856564 @default.
• W4225295159 cites W3091119025 @default.
• W4225295159 cites W3094434608 @default.
• W4225295159 cites W3096802605 @default.
• W4225295159 cites W3100870891 @default.
• W4225295159 cites W3102501754 @default.
• W4225295159 cites W3105214773 @default.
• W4225295159 cites W3118610157 @default.
• W4225295159 cites W3123543052 @default.
• W4225295159 cites W3125841070 @default.
• W4225295159 cites W3155737436 @default.
• W4225295159 cites W3171579381 @default.
• W4225295159 cites W3199377726 @default.
• W4225295159 cites W3214813820 @default.
• W4225295159 cites W4200438025 @default.
• W4225295159 cites W4200447609 @default.
• W4225295159 cites W4206143925 @default.
• W4225295159 cites W4210371746 @default.
• W4225295159 doi "https://doi.org/10.3390/math10091536" @default.
• W4225295159 hasPublicationYear "2022" @default.
• W4225295159 type Work @default.
• W4225295159 citedByCount "6" @default.
• W4225295159 countsByYear W42252951592022 @default.
• W4225295159 countsByYear W42252951592023 @default.
• W4225295159 crossrefType "journal-article" @default.
• W4225295159 hasAuthorship W4225295159A5002524886 @default.
• W4225295159 hasAuthorship W4225295159A5050153883 @default.
• W4225295159 hasAuthorship W4225295159A5080344855 @default.
• W4225295159 hasAuthorship W4225295159A5083173630 @default.
• W4225295159 hasBestOaLocation W42252951591 @default.
• W4225295159 hasConcept C118487528 @default.
• W4225295159 hasConcept C134018914 @default.
• W4225295159 hasConcept C154945302 @default.
• W4225295159 hasConcept C169760540 @default.
• W4225295159 hasConcept C2777093970 @default.
• W4225295159 hasConcept C2779829184 @default.
• W4225295159 hasConcept C2780261187 @default.
• W4225295159 hasConcept C2780347916 @default.
• W4225295159 hasConcept C2780827179 @default.
• W4225295159 hasConcept C31972630 @default.
• W4225295159 hasConcept C34736171 @default.
• W4225295159 hasConcept C41008148 @default.
• W4225295159 hasConcept C555293320 @default.
• W4225295159 hasConcept C71924100 @default.
• W4225295159 hasConcept C86803240 @default.
• W4225295159 hasConcept C89600930 @default.
• W4225295159 hasConceptScore W4225295159C118487528 @default.

• next