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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 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

This 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 AP2009-2835

© IEEE Journal of Biomedical and Health Informatics, 2015, vol. 19, núm. 1, p. 349-357

Institute of Electrical and Electronics Engineers (IEEE)

Manager: Ministerio de Economía y Competitividad (Espanya)
Author: Gubern Mérida, Albert
Kallenberg, Michiel
Mann, Ritse M.
Martí Marly, Robert
Karssemeijer, Nico
Date: 2015 January 1
Abstract: 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
This 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 AP2009-2835
Format: application/pdf
Citation: 022079
ISSN: 2168-2194 (versió paper)
2168-2208 (versió electrònica)
Document access: http://hdl.handle.net/10256/10919
Language: eng
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Collection: MINECO/PN 2013-2015/TIN2012-37171-C02-01
Reproducció digital del document publicat a: http://dx.doi.org/10.1109/JBHI.2014.2311163
Articles publicats (D-ATC)
Is part of: © IEEE Journal of Biomedical and Health Informatics, 2015, vol. 19, núm. 1, p. 349-357
Rights: Tots els drets reservats
Subject: Imatges digitals
Digital images
Imatgeria mèdica
Imaging systems in medicine
Mama -- Càncer -- Imatgeria
Breast -- Cancer -- Imaging
Mama -- Radiografia
Breast -- Radiography
Title: Breast segmentation and density estimation in breast MRI: A fully automatic framework
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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