Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Automated segmentation of retinal layers in optical coherence tomography images using Xception70 feature extraction

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Applied soft computing

ISSN

1568-4946

e-ISSN

1872-9681

Svazek periodika

167

Číslo periodika v rámci svazku

December

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

"Article number: 112414"

Kód UT WoS článku

001349715600001

EID výsledku v databázi Scopus

2-s2.0-85207925487