A multi-task convolutional neural network for classification and segmentation of chronic venous disorders
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/90506 |
Resumo: | Supplementary Information: The online version contains supplementary material available at https://doi.org/ 10.1038/s41598-022-27089-8. |
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A multi-task convolutional neural network for classification and segmentation of chronic venous disordersAgedHumansEuropeImage Processing, Computer-AssistedNorth AmericaChronic DiseaseCardiovascular DiseasesNeural Networks, ComputerVeinsEngenharia e Tecnologia::Engenharia MédicaScience & TechnologySaúde de qualidadeSupplementary Information: The online version contains supplementary material available at https://doi.org/ 10.1038/s41598-022-27089-8.Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician's expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.Te authors acknowledge Fundação para a Ciência e a Tecnologia (FCT), Portugal and the European Social Found, European Union, for funding support through the “Programa Operacional Capital Humano” (POCH) in the scope of the PhD grants SFRH/BD/136721/2018 (B. Oliveira), SFRH/BD/136670/2018 (H. Torres), and SFRH/BD/131545/2017 (F. Veloso). Moreover, authors gratefully acknowledge the funding of the projects "NORTE 01-0145-FEDER-000045” and "NORTE-01-0145-FEDER-000059", supported by Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the Euro pean Regional Development Fund (FEDER). It was also funded by national funds, through the FCT and FCT/MCTES in the scope of the project UIDB/05549/2020 and UIDP/05549/2020. Te authors would like to thank Ederson A. G. Dorileo, and co-authors for providing the ULCER dataset. Moreover, the authors also would like to thank Xiaoxiao Sun and co-authors for providing the SD-198 dataset. Finally, the authors would like to thank you Neusa Tenreiro and other healthcare that helped on the acquisition of the images.Nature ResearchUniversidade do MinhoOliveira, BrunoTorres, Helena RMorais, PedroVeloso, FernandoBaptista, António L.Fonseca, Jaime C.Vilaça, João L.2023-12-142023-12-14T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/90506engOliveira, B., Torres, H.R., Morais, P. et al. A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Sci Rep 13, 761 (2023). https://doi.org/10.1038/s41598-022-27089-82045-232210.1038/s41598-022-27089-836641527761https://www.nature.com/articles/s41598-022-27089-8info: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:RCAAP2024-05-11T05:36:01Zoai:repositorium.sdum.uminho.pt:1822/90506Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T05:36:01Repositório 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 multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
title |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
spellingShingle |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders Oliveira, Bruno Aged Humans Europe Image Processing, Computer-Assisted North America Chronic Disease Cardiovascular Diseases Neural Networks, Computer Veins Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
title_short |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
title_full |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
title_fullStr |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
title_full_unstemmed |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
title_sort |
A multi-task convolutional neural network for classification and segmentation of chronic venous disorders |
author |
Oliveira, Bruno |
author_facet |
Oliveira, Bruno Torres, Helena R Morais, Pedro Veloso, Fernando Baptista, António L. Fonseca, Jaime C. Vilaça, João L. |
author_role |
author |
author2 |
Torres, Helena R Morais, Pedro Veloso, Fernando Baptista, António L. Fonseca, Jaime C. Vilaça, João L. |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Oliveira, Bruno Torres, Helena R Morais, Pedro Veloso, Fernando Baptista, António L. Fonseca, Jaime C. Vilaça, João L. |
dc.subject.por.fl_str_mv |
Aged Humans Europe Image Processing, Computer-Assisted North America Chronic Disease Cardiovascular Diseases Neural Networks, Computer Veins Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
topic |
Aged Humans Europe Image Processing, Computer-Assisted North America Chronic Disease Cardiovascular Diseases Neural Networks, Computer Veins Engenharia e Tecnologia::Engenharia Médica Science & Technology Saúde de qualidade |
description |
Supplementary Information: The online version contains supplementary material available at https://doi.org/ 10.1038/s41598-022-27089-8. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-12-14 2023-12-14T00:00:00Z |
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 |
https://hdl.handle.net/1822/90506 |
url |
https://hdl.handle.net/1822/90506 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Oliveira, B., Torres, H.R., Morais, P. et al. A multi-task convolutional neural network for classification and segmentation of chronic venous disorders. Sci Rep 13, 761 (2023). https://doi.org/10.1038/s41598-022-27089-8 2045-2322 10.1038/s41598-022-27089-8 36641527 761 https://www.nature.com/articles/s41598-022-27089-8 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Nature Research |
publisher.none.fl_str_mv |
Nature Research |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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 |
repository.mail.fl_str_mv |
mluisa.alvim@gmail.com |
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1817544675006873600 |