Evaluation of the impact of domain adaptation on segmentation of Multiple Sclerosis lesions in MRI

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorFaculty of Engineering-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorINESC TEC and Faculty of Engineering-
Autor(es): dc.creatorSousa, Isabella Medeiros De-
Autor(es): dc.creatorDe Oliveira, Marcela-
Autor(es): dc.creatorLisboa-Filho, Paulo Noronha-
Autor(es): dc.creatorCardoso, Jaime Dos Santos-
Data de aceite: dc.date.accessioned2025-08-21T19:43:49Z-
Data de disponibilização: dc.date.available2025-08-21T19:43:49Z-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2022-04-28-
Data de envio: dc.date.issued2020-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/BIBM52615.2021.9669533-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/223511-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/223511-
Descrição: dc.descriptionMultiple Sclerosis (MS) is a chronic and inflammatory disorder that causes degeneration of axons in brain white matter and spinal cord. Magnetic Resonance Imaging (MRI) is extensively used to identify MS lesions and evaluate the progression of the disease, but the manual identification and quantification of lesions are time consuming and error-prone tasks. Thus, automated Deep Learning methods, in special Convolutional Neural Networks (CNNs), are becoming popular to segment medical images. It has been noticed that the performance of those methods tends to decrease when applied to MRI acquired under different protocols. The aim of this work is to statistically evaluate the possible influence of domain adaptation during the training process of CNNs models for segmenting MS lesions in MRI. The segmentation models were tested on MRIs (FLAIR and T1) of 20 patients diagnosed with Multiple Sclerosis. The set of segmented images of each different model was compared statistically, through the metrics Dice Similarity Coefficient (DSC), Predictive Positive Value (PPV) and Absolute Volume Difference (AVD). The results indicate that the domain adapted training can improve the performance of automatic segmentation methods, by CNNs, and have great potential to be used in medical clinics in the future.-
Descrição: dc.descriptionUniversity of Porto Faculty of Engineering-
Descrição: dc.descriptionState University (UNESP) School of Sciences-São Paulo-
Descrição: dc.descriptionUniversity of Porto INESC TEC and Faculty of Engineering-
Descrição: dc.descriptionState University (UNESP) School of Sciences-São Paulo-
Formato: dc.format1786-1790-
Idioma: dc.languageen-
Relação: dc.relationProceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021-
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Palavras-chave: dc.subjectConvolutional Neural Networks-
Palavras-chave: dc.subjectdomain adaptation-
Palavras-chave: dc.subjectMRI-
Palavras-chave: dc.subjectmultiple sclerosis-
Palavras-chave: dc.subjectsegmentation-
Título: dc.titleEvaluation of the impact of domain adaptation on segmentation of Multiple Sclerosis lesions in MRI-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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