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DC Field | Value | Language |
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dc.contributor | Sistema FMUSP-HC: Faculdade de Medicina da Universidade de São Paulo (FMUSP) e Hospital das Clínicas da FMUSP | |
dc.contributor.author | GRAVES, Catharine V. | |
dc.contributor.author | MORENO, Ramon A. | |
dc.contributor.author | REBELO, Marina S. | |
dc.contributor.author | NOMURA, Cesar H. | |
dc.contributor.author | GUTIERREZ, Marco A. | |
dc.date.accessioned | 2021-06-14T13:30:37Z | - |
dc.date.available | 2021-06-14T13:30:37Z | - |
dc.date.issued | 2020 | |
dc.identifier.citation | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, p.1203-1206, 2020 | |
dc.identifier.isbn | 978-1-7281-1990-8 | |
dc.identifier.issn | 1557-170X | |
dc.identifier.uri | https://observatorio.fm.usp.br/handle/OPI/40454 | - |
dc.description.abstract | Cardiovascular disease is one of the major health problems worldwide. In clinical practice, cardiac magnetic resonance imaging (CMR) is considered the gold-standard imaging modality for the evaluation of the function and structure of the left ventricle (LV). More recently, deep learning methods have been used to segment LV with impressive results. On the other hand, this kind of approach is prone to overfit the training data, and it does not generalize well between different data acquisition centers, thus creating constraints to the use in daily routines. In this paper, we explore methods to improve the generalization in the segmentation performed by a convolutional neural network. We applied a U-net based architecture and compared two different pre-processing methods to improve uniformity in the image contrast between five cross-dataset training and testing. Overall, we were able to perform the segmentation of the left ventricle using multiple cross-dataset combinations of train and test, with a mean endocardium dice score of 0.82. | eng |
dc.description.sponsorship | Canon Medical System Corp. | |
dc.description.sponsorship | Zerbini Foundation | |
dc.language.iso | eng | |
dc.publisher | IEEE | eng |
dc.relation.ispartof | 42nd Annual International Conferences of the Ieee Engineering in Medicine and Biology Society: Enabling Innovative Technologies for Global Healthcare Embc'20 | |
dc.relation.ispartofseries | IEEE Engineering in Medicine and Biology Society Conference Proceedings | |
dc.rights | restrictedAccess | eng |
dc.title | Improving the generalization of deep learning methods to segment the left ventricle in short axis MR images | eng |
dc.type | conferenceObject | eng |
dc.rights.holder | Copyright IEEE | eng |
dc.description.conferencedate | JUL 20-24, 2020 | |
dc.description.conferencelocal | Montreal, CANADA | |
dc.description.conferencename | 42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) | |
dc.subject.wos | Engineering, Biomedical | eng |
dc.subject.wos | Engineering, Electrical & Electronic | eng |
dc.type.category | proceedings paper | eng |
dc.type.version | publishedVersion | eng |
hcfmusp.description.beginpage | 1203 | |
hcfmusp.description.endpage | 1206 | |
hcfmusp.origem | WOS | |
hcfmusp.origem.id | WOS:000621592201130 | |
hcfmusp.publisher.city | NEW YORK | eng |
hcfmusp.publisher.country | USA | eng |
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hcfmusp.relation.reference | World Health Organization, 2017, CARD DIS CVDS | eng |
hcfmusp.relation.reference | Yang S. L. Guanyu, LEFT VENTRICLE FULL | eng |
dc.description.index | PubMed | eng |
dc.identifier.eissn | 1558-4615 | |
Appears in Collections: | Comunicações em Eventos - HC/InCor Comunicações em Eventos - LIM/65 |
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art_GRAVES_Improving_the_generalization_of_deep_learning_methods_to_2020.PDF Restricted Access | publishedVersion (English) | 263.24 kB | Adobe PDF | View/Open Request a copy |
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