A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images

Detalhes bibliográficos
Autor(a) principal: Philippi, Daniel
Data de Publicação: 2023
Outros Autores: Rothaus, Kai, Castelli, Mauro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/148025
Resumo: Philippi, D., Rothaus, K., & Castelli, M. (2023). A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images. Scientific Reports, 13(1), 1-14. [517]. https://doi.org/10.1038/s41598-023-27616-1 --- Funding Information: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
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spelling A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography imagesGeneralSDG 3 - Good Health and Well-beingPhilippi, D., Rothaus, K., & Castelli, M. (2023). A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images. Scientific Reports, 13(1), 1-14. [517]. https://doi.org/10.1038/s41598-023-27616-1 --- Funding Information: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network’s architecture to increase its segmentation performance while maintaining its computational efficiency.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNPhilippi, DanielRothaus, KaiCastelli, Mauro2023-01-23T22:16:11Z2023-01-102023-01-10T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/148025eng2045-2322PURE: 51327230https://doi.org/10.1038/s41598-023-27616-1info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:29:17Zoai:run.unl.pt:10362/148025Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:09.569299Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
title A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
spellingShingle A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
Philippi, Daniel
General
SDG 3 - Good Health and Well-being
title_short A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
title_full A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
title_fullStr A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
title_full_unstemmed A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
title_sort A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images
author Philippi, Daniel
author_facet Philippi, Daniel
Rothaus, Kai
Castelli, Mauro
author_role author
author2 Rothaus, Kai
Castelli, Mauro
author2_role author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Philippi, Daniel
Rothaus, Kai
Castelli, Mauro
dc.subject.por.fl_str_mv General
SDG 3 - Good Health and Well-being
topic General
SDG 3 - Good Health and Well-being
description Philippi, D., Rothaus, K., & Castelli, M. (2023). A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images. Scientific Reports, 13(1), 1-14. [517]. https://doi.org/10.1038/s41598-023-27616-1 --- Funding Information: This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Mauro Castelli acknowledges the financial support from the Slovenian Research Agency (research core funding no. P5-0410).
publishDate 2023
dc.date.none.fl_str_mv 2023-01-23T22:16:11Z
2023-01-10
2023-01-10T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/148025
url http://hdl.handle.net/10362/148025
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2045-2322
PURE: 51327230
https://doi.org/10.1038/s41598-023-27616-1
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 14
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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instacron:RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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