Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00843989%3A_____%2F24%3AE0111539" target="_blank" >RIV/00843989:_____/24:E0111539 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2590123024013094?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2590123024013094?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2024.103054" target="_blank" >10.1016/j.rineng.2024.103054</a>

Result language

angličtina

Original language name

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

Original language description

Background: ROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. Objective: This study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. Methods: The pipeline uses different methods, including CIELAB Enhancement, Background Normalization, BellShaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pretrained Convolution Neural Network architectures to evaluate its automatic classification capabilities. Results: The pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. Conclusion: The segm...

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30207 - Ophthalmology

Project

—

Continuities

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Publication year

2024

Confidentiality

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

Name of the periodical

Results in engineering

ISSN

2590-1230

e-ISSN

2590-1230

Volume of the periodical

24

Issue of the periodical within the volume

article 103054

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

17

Pages from-to

1-17

UT code for WoS article

001334066600001

EID of the result in the Scopus database

2-s2.0-85205763616