Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50021880" target="_blank" >RIV/62690094:18470/24:50021880 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.asoc.2024.112414" target="_blank" >https://doi.org/10.1016/j.asoc.2024.112414</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2024.112414" target="_blank" >10.1016/j.asoc.2024.112414</a>
Alternative languages
Result language
angličtina
Original language name
Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction
Original language description
Optical coherence tomography (OCT) imaging plays a critical role in evaluating retinal layer thickness, serving as a pivotal diagnostic tool for numerous retinal conditions. However, challenges such as speckle noise, poor image contrast, and ambiguous retinal detachments, like drusen, often hinder accurate segmentation of retinal layers. To address these challenges and enhance diagnostic precision, we introduce the Retinal Segmentation Network, or “Ret-Seg Net,” a deep neural network-based approach. Leveraging the advanced Xception70 feature extractor, Ret-Seg Net extracts and comprehends the intricate properties of retinal layers. By integrating acquired feature maps from Xception70 into the atrous spatial pyramid-pooling module, Ret-Seg Net extracts multiscale feature information. The encoder-decoder module of Ret-Seg Net achieves automated segmentation of retinal layers in OCT images by reconstructing distinct retinal layer borders. This advanced module accurately recognizes and differentiates retinal layer boundaries, providing precise and reliable segmentation. Validation of our approach using real-time images and the Duke dataset, comprising 310 volumes with 40 B-scans each, demonstrates outstanding performance. Mean intersection over union (MIoU) and sensitivity (Se) metrics achieved remarkable values of 94.52 % and 96.25 % respectively. Furthermore, our approach offers a versatile segmentation framework applicable to various tissues and cell types in clinical settings. Automating segmentation of retinal layers enhances precision in disease identification and monitoring while significantly improving labor efficiency by reducing the need for manual segmentation.
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
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied soft computing
ISSN
1568-4946
e-ISSN
1872-9681
Volume of the periodical
167
Issue of the periodical within the volume
December
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
"Article number: 112414"
UT code for WoS article
001349715600001
EID of the result in the Scopus database
2-s2.0-85207925487