Breast segmentation and density estimation in breast MRI: A fully automatic framework

Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image proc...

Full description

Bibliographic Details
Authors: Gubern Mérida, Albert, Kallenberg, Michiel, Mann, Ritse M., Martí Marly, Robert, Karssemeijer, Nico
Format: article
Status:Published version
Publication Date:2015
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/10919
Online Access:http://hdl.handle.net/10256/10919
Access Level:Embargoed access
Keyword:Imatges digitals
Digital images
Imatgeria mèdica
Imaging systems in medicine
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Mama -- Radiografia
Breast -- Radiography
id ES_7d259afbc3ef8ac6db8f7bbe828e9f75
oai_identifier_str oai:recercat.cat:10256/10919
network_acronym_str ES
network_name_str España
repository_id_str
spelling Breast segmentation and density estimation in breast MRI: A fully automatic frameworkGubern Mérida, AlbertKallenberg, MichielMann, Ritse M.Martí Marly, RobertKarssemeijer, NicoImatges digitalsDigital imagesImatgeria mèdicaImaging systems in medicineMama -- Càncer -- ImatgeriaBreast -- Cancer -- ImagingMama -- RadiografiaBreast -- RadiographyBreast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image processing techniques to segment breast and fibroglandular tissue. Intra- and interpatient signal intensity variability is initially corrected. The breast is segmented by automatically detecting body-breast and air-breast surfaces. Subsequently, fibroglandular tissue is segmented in the breast area using expectation-maximization. A dataset of 50 cases with manual segmentations was used for evaluation. Dice similarity coefficient (DSC), total overlap, false negative fraction (FNF), and false positive fraction (FPF) are used to report similarity between automatic and manual segmentations. For breast segmentation, the proposed approach obtained DSC, total overlap, FNF, and FPF values of 0.94, 0.96, 0.04, and 0.07, respectively. For fibroglandular tissue segmentation, we obtained DSC, total overlap, FNF, and FPF values of 0.80, 0.85, 0.15, and 0.22, respectively. The method is relevant for researchers investigating breast density as a risk factor for breast cancer and all the described steps can be also applied in computer aided diagnosis systemsThis work was supported by the Spanish Science and Innovation under Grant TIN2012-37171-C02-01. The work of A. Gubern-Merida was supported by the FPU under Grant AP2009-2835Institute of Electrical and Electronics Engineers (IEEE)Ministerio de Economía y Competitividad (Espanya)infoinfo2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10256/10919http://hdl.handle.net/10256/10919© IEEE Journal of Biomedical and Health Informatics, 2015, vol. 19, núm. 1, p. 349-357Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1109/JBHI.2014.2311163info:eu-repo/semantics/altIdentifier/issn/2168-2194info:eu-repo/semantics/altIdentifier/eissn/2168-2208info:eu-repo/grantAgreement/MINECO//TIN2012-37171-C02-01Tots els drets reservatsinfo:eu-repo/semantics/embargoedAccessoai:recercat.cat:10256/109192026-05-29T05:05:01Z
dc.title.none.fl_str_mv Breast segmentation and density estimation in breast MRI: A fully automatic framework
title Breast segmentation and density estimation in breast MRI: A fully automatic framework
spellingShingle Breast segmentation and density estimation in breast MRI: A fully automatic framework
Gubern Mérida, Albert
Imatges digitals
Digital images
Imatgeria mèdica
Imaging systems in medicine
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Mama -- Radiografia
Breast -- Radiography
title_short Breast segmentation and density estimation in breast MRI: A fully automatic framework
title_full Breast segmentation and density estimation in breast MRI: A fully automatic framework
title_fullStr Breast segmentation and density estimation in breast MRI: A fully automatic framework
title_full_unstemmed Breast segmentation and density estimation in breast MRI: A fully automatic framework
title_sort Breast segmentation and density estimation in breast MRI: A fully automatic framework
dc.creator.none.fl_str_mv Gubern Mérida, Albert
Kallenberg, Michiel
Mann, Ritse M.
Martí Marly, Robert
Karssemeijer, Nico
author Gubern Mérida, Albert
author_facet Gubern Mérida, Albert
Kallenberg, Michiel
Mann, Ritse M.
Martí Marly, Robert
Karssemeijer, Nico
author_role author
author2 Kallenberg, Michiel
Mann, Ritse M.
Martí Marly, Robert
Karssemeijer, Nico
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Economía y Competitividad (Espanya)
dc.subject.none.fl_str_mv Imatges digitals
Digital images
Imatgeria mèdica
Imaging systems in medicine
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Mama -- Radiografia
Breast -- Radiography
topic Imatges digitals
Digital images
Imatgeria mèdica
Imaging systems in medicine
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Mama -- Radiografia
Breast -- Radiography
description Breast density measurement is an important aspect in breast cancer diagnosis as dense tissue has been related to the risk of breast cancer development. The purpose of this study is to develop a method to automatically compute breast density in breast MRI. The framework is a combination of image processing techniques to segment breast and fibroglandular tissue. Intra- and interpatient signal intensity variability is initially corrected. The breast is segmented by automatically detecting body-breast and air-breast surfaces. Subsequently, fibroglandular tissue is segmented in the breast area using expectation-maximization. A dataset of 50 cases with manual segmentations was used for evaluation. Dice similarity coefficient (DSC), total overlap, false negative fraction (FNF), and false positive fraction (FPF) are used to report similarity between automatic and manual segmentations. For breast segmentation, the proposed approach obtained DSC, total overlap, FNF, and FPF values of 0.94, 0.96, 0.04, and 0.07, respectively. For fibroglandular tissue segmentation, we obtained DSC, total overlap, FNF, and FPF values of 0.80, 0.85, 0.15, and 0.22, respectively. The method is relevant for researchers investigating breast density as a risk factor for breast cancer and all the described steps can be also applied in computer aided diagnosis systems
publishDate 2015
dc.date.none.fl_str_mv 2015
info
info
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/10919
http://hdl.handle.net/10256/10919
url http://hdl.handle.net/10256/10919
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/JBHI.2014.2311163
info:eu-repo/semantics/altIdentifier/issn/2168-2194
info:eu-repo/semantics/altIdentifier/eissn/2168-2208
info:eu-repo/grantAgreement/MINECO//TIN2012-37171-C02-01
dc.rights.none.fl_str_mv Tots els drets reservats
info:eu-repo/semantics/embargoedAccess
rights_invalid_str_mv Tots els drets reservats
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers (IEEE)
dc.source.none.fl_str_mv © IEEE Journal of Biomedical and Health Informatics, 2015, vol. 19, núm. 1, p. 349-357
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411642540818432
score 15.812429

Similar Items