Retinal Vessel Segmentation by U-Net with VGG-16 Backbone on Patched Images with Smooth Blending

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10254784" target="_blank" >RIV/61989100:27240/23:10254784 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68145535:_____/23:00585312

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-40971-4_44" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-40971-4_44</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-40971-4_44" target="_blank" >10.1007/978-3-031-40971-4_44</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Retinal Vessel Segmentation by U-Net with VGG-16 Backbone on Patched Images with Smooth Blending

Popis výsledku v původním jazyce

Detecting vessels in retinal images is crucial for various medical applications, including diagnosing and monitoring eye diseases such as diabetic retinopathy, glaucoma, and macular degeneration. This paper presents a study on applying the U-Net architecture with a VGG-16 backbone for retinal vessel segmentation trained on patched images. As a source of training images, three well-labeled datasets, DRIVE, STARE, and CHASE DB1, were used for the training of the segmentation algorithm. We implemented the task-specific data class to further divide training images into patches, and the data augmentation techniques to increase the size of training set and to promote the model&apos;s generalization ability. Additionally, a blending technique was employed to achieve smooth predictions by blending image patches. The experimental results highlight the effectiveness of the proposed approach in accurately detecting blood vessels in retinal images, providing promising prospects for improving ophthalmic diagnosis and treatment.

Název v anglickém jazyce

Retinal Vessel Segmentation by U-Net with VGG-16 Backbone on Patched Images with Smooth Blending

Popis výsledku anglicky

Detecting vessels in retinal images is crucial for various medical applications, including diagnosing and monitoring eye diseases such as diabetic retinopathy, glaucoma, and macular degeneration. This paper presents a study on applying the U-Net architecture with a VGG-16 backbone for retinal vessel segmentation trained on patched images. As a source of training images, three well-labeled datasets, DRIVE, STARE, and CHASE DB1, were used for the training of the segmentation algorithm. We implemented the task-specific data class to further divide training images into patches, and the data augmentation techniques to increase the size of training set and to promote the model&apos;s generalization ability. Additionally, a blending technique was employed to achieve smooth predictions by blending image patches. The experimental results highlight the effectiveness of the proposed approach in accurately detecting blood vessels in retinal images, providing promising prospects for improving ophthalmic diagnosis and treatment.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes on Data Engineering and Communications Technologies. Volume 182

ISBN

978-3-031-40970-7

ISSN

2367-4512

e-ISSN

2367-4520

Počet stran výsledku

10

Strana od-do

465-474

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Čiang Mai

Datum konání akce

6. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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