Retinal blood vessel segmentation and inpainting networks with multi-level self-attention
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10490445" target="_blank" >RIV/00216208:11320/25:10490445 - isvavai.cz</a>
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ykXO_e.lP9" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=ykXO_e.lP9</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bspc.2024.107343" target="_blank" >10.1016/j.bspc.2024.107343</a>
Alternative languages
Result language
angličtina
Original language name
Retinal blood vessel segmentation and inpainting networks with multi-level self-attention
Original language description
Improvement and restoration of retinal images are vital for clinical applications, from abnormality classification through segmentation to automated medical diagnosis. The major problem of retina restoration is estimating the image regions obscured by unwanted features, of which the most significant culprit is the blood vessel network. The challenge lies in the unavailability of true, unobstructed images. The commonly used methods apply masked filtering, dictionary-based approaches, or rely on an innate ability of machine learning models to deal with blood vessels. To solve the blind blood vessel inpainting problem, we propose a convolutional network architecture with multi-level self-attention capable of learning both segmentation and inpainting of blood vessels in retinal images. Furthermore, we introduce an efficient training method for the inpainting task with unknown ground truth. Our focus is on the optic nerve head region, which is essential in fundus analysis and glaucoma diagnosis. Our approach surpasses the state-of-the-art methods in blood vessel inpainting on the examined data while being trainable on personal computers. We examine the accuracy of vessel segmentation and the quality of inpainted images produced by our approach. The results show a statistically significant increase in segmentation accuracy of traditional methods after inpainting. In conclusion, we present a reliable vessel removal method applicable as a crucial first step in retinal segmentation, in the shape and color analysis of separated retinal vessels and background, in blood vessel detection, or in generating clear retinal background for generative methods.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Biomedical Signal Processing and Control
ISSN
1746-8094
e-ISSN
1746-8108
Volume of the periodical
102
Issue of the periodical within the volume
April
Country of publishing house
GB - UNITED KINGDOM
Number of pages
9
Pages from-to
107343
UT code for WoS article
001391256300001
EID of the result in the Scopus database
2-s2.0-85212565292