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A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

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A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation

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dc.contributor.author Perez-Benito, Francisco Javier es_ES
dc.contributor.author Signol, François es_ES
dc.contributor.author Perez-Cortes, Juan-Carlos es_ES
dc.contributor.author Fuster Bagetto, Alejandro es_ES
dc.contributor.author Pollan, Marina es_ES
dc.contributor.author Pérez-Gómez, Beatriz es_ES
dc.contributor.author Salas-Trejo, Dolores es_ES
dc.contributor.author Casals, Maria es_ES
dc.contributor.author Martínez, Inmaculada es_ES
dc.contributor.author Llobet Azpitarte, Rafael es_ES
dc.date.accessioned 2021-07-01T03:32:53Z
dc.date.available 2021-07-01T03:32:53Z
dc.date.issued 2020-10 es_ES
dc.identifier.issn 0169-2607 es_ES
dc.identifier.uri http://hdl.handle.net/10251/168615
dc.description.abstract [EN] Background and Objective: Breast cancer is the most frequent cancer in women. The Spanish healthcare network established population-based screening programs in all Autonomous Communities, where mammograms of asymptomatic women are taken with early diagnosis purposes. Breast density assessed from digital mammograms is a biomarker known to be related to a higher risk to develop breast cancer. It is thus crucial to provide a reliable method to measure breast density from mammograms. Furthermore the complete automation of this segmentation process is becoming fundamental as the amount of mammograms increases every day. Important challenges are related with the differences in images from different devices and the lack of an objective gold standard. This paper presents a fully automated framework based on deep learning to estimate the breast density. The framework covers breast detection, pectoral muscle exclusion, and fibroglandular tissue segmentation. Methods: A multi-center study, composed of 1785 women whose "for presentation" mammograms were segmented by two experienced radiologists. A total of 4992 of the 6680 mammograms were used as training corpus and the remaining (1688) formed the test corpus. This paper presents a histogram normalization step that smoothed the difference between acquisition, a regression architecture that learned segmentation parameters as intrinsic image features and a loss function based on the DICE score. Results: The results obtained indicate that the level of concordance (DICE score) reached by the two radiologists (0.77) was also achieved by the automated framework when it was compared to the closest breast segmentation from the radiologists. For the acquired with the highest quality device, the DICE score per acquisition device reached 0.84, while the concordance between radiologists was 0.76. Conclusions: An automatic breast density estimator based on deep learning exhibits similar performance when compared with two experienced radiologists. It suggests that this system could be used to support radiologists to ease its work. es_ES
dc.description.sponsorship This work was partially funded by Generalitat Valenciana through I+D IVACE (Valencian Institute of Business Competitiviness) and GVA (European Regional Development Fund) supports under the project IMAMCN/2019/1, and by Carlos III Institute of Health under the project DTS15/00080. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computer Methods and Programs in Biomedicine es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Breast density es_ES
dc.subject Entirely convolutional neural network (ECNN) es_ES
dc.subject Deep learning es_ES
dc.subject Dense tissue segmentation es_ES
dc.subject Mammography es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.cmpb.2020.105668 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/IVACE//IMAMCN%2F2019%2F1/ES/Plan de Actividades de carácter no económico 2019/EMOSPACES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DTS15%2F00080/ES/DM-Scan: herramienta de lectura de densidad mamográfica como fenotipo marcador de riesgo de cáncer de mama/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Perez-Benito, FJ.; Signol, F.; Perez-Cortes, J.; Fuster Bagetto, A.; Pollan, M.; Pérez-Gómez, B.; Salas-Trejo, D.... (2020). A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation. Computer Methods and Programs in Biomedicine. 195:123-132. https://doi.org/10.1016/j.cmpb.2020.105668 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.cmpb.2020.105668 es_ES
dc.description.upvformatpinicio 123 es_ES
dc.description.upvformatpfin 132 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 195 es_ES
dc.relation.pasarela S\417287 es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Institut Valencià de Competitivitat Empresarial es_ES
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