Automated assessment of area of dense tissue in the breast: A comparison with human estimation

Camilla Jeffries-Chung; Jennifer Diffey; Michael Berks; Joanna Morrison; Rosanne Verow; Julie Morris; Mary Wilson; Caroline Boggis; Nicky Barr; Anthony Maxwell; Alan Hufton; Susan Astley

doi:10.1007/978-3-642-13666-5_23

Automated assessment of area of dense tissue in the breast: A comparison with human estimation

Camilla Jeffries-Chung, Jennifer Diffey, Michael Berks, Joanna Morrison, Rosanne Verow, Julie Morris, Mary Wilson, Caroline Boggis, Nicky Barr, Anthony Maxwell, Alan Hufton, Susan Astley

Research output: Contribution to journal › Article › peer-review

Abstract

Both interactive thresholding tools and human visual assessment have been related to the risk of developing breast cancer. In this paper we explore the relationship between human assessment of area of dense tissue and the actual thickness of tissue in the breast by using a volumetric density technique to compute areas of dense tissue, varying the threshold below which areas of low density are discounted and observing the correlation with visual assessment of density at different thresholds. Based on analysis of thresholds used in the automated method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10% and 20% of the total thickness of the compressed breast at that point. © 2010 Springer-Verlag.

Original language	English
Pages (from-to)	168-174
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6136
DOIs	https://doi.org/10.1007/978-3-642-13666-5_23
Publication status	Published - 2010

Keywords

breast density
computer analysis
human perception
risk assessment

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-642-13666-5_23

Cite this

Jeffries-Chung, C., Diffey, J., Berks, M., Morrison, J., Verow, R., Morris, J., Wilson, M., Boggis, C., Barr, N., Maxwell, A., Hufton, A., & Astley, S. (2010). Automated assessment of area of dense tissue in the breast: A comparison with human estimation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6136, 168-174. https://doi.org/10.1007/978-3-642-13666-5_23

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title = "Automated assessment of area of dense tissue in the breast: A comparison with human estimation",

abstract = "Both interactive thresholding tools and human visual assessment have been related to the risk of developing breast cancer. In this paper we explore the relationship between human assessment of area of dense tissue and the actual thickness of tissue in the breast by using a volumetric density technique to compute areas of dense tissue, varying the threshold below which areas of low density are discounted and observing the correlation with visual assessment of density at different thresholds. Based on analysis of thresholds used in the automated method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10% and 20% of the total thickness of the compressed breast at that point. {\textcopyright} 2010 Springer-Verlag.",

keywords = "breast density, computer analysis, human perception, risk assessment",

author = "Camilla Jeffries-Chung and Jennifer Diffey and Michael Berks and Joanna Morrison and Rosanne Verow and Julie Morris and Mary Wilson and Caroline Boggis and Nicky Barr and Anthony Maxwell and Alan Hufton and Susan Astley",

year = "2010",

doi = "10.1007/978-3-642-13666-5_23",

language = "English",

volume = "6136",

pages = "168--174",

journal = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Jeffries-Chung, C, Diffey, J, Berks, M, Morrison, J, Verow, R, Morris, J, Wilson, M, Boggis, C, Barr, N, Maxwell, A, Hufton, A & Astley, S 2010, 'Automated assessment of area of dense tissue in the breast: A comparison with human estimation', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6136, pp. 168-174. https://doi.org/10.1007/978-3-642-13666-5_23

Automated assessment of area of dense tissue in the breast: A comparison with human estimation. / Jeffries-Chung, Camilla; Diffey, Jennifer; Berks, Michael et al.
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6136, 2010, p. 168-174.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Automated assessment of area of dense tissue in the breast: A comparison with human estimation

AU - Jeffries-Chung, Camilla

AU - Diffey, Jennifer

AU - Berks, Michael

AU - Morrison, Joanna

AU - Verow, Rosanne

AU - Morris, Julie

AU - Wilson, Mary

AU - Boggis, Caroline

AU - Barr, Nicky

AU - Maxwell, Anthony

AU - Hufton, Alan

AU - Astley, Susan

PY - 2010

Y1 - 2010

N2 - Both interactive thresholding tools and human visual assessment have been related to the risk of developing breast cancer. In this paper we explore the relationship between human assessment of area of dense tissue and the actual thickness of tissue in the breast by using a volumetric density technique to compute areas of dense tissue, varying the threshold below which areas of low density are discounted and observing the correlation with visual assessment of density at different thresholds. Based on analysis of thresholds used in the automated method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10% and 20% of the total thickness of the compressed breast at that point. © 2010 Springer-Verlag.

AB - Both interactive thresholding tools and human visual assessment have been related to the risk of developing breast cancer. In this paper we explore the relationship between human assessment of area of dense tissue and the actual thickness of tissue in the breast by using a volumetric density technique to compute areas of dense tissue, varying the threshold below which areas of low density are discounted and observing the correlation with visual assessment of density at different thresholds. Based on analysis of thresholds used in the automated method, radiologists' definition of a dense pixel is one in which the percentage of glandular tissue is between 10% and 20% of the total thickness of the compressed breast at that point. © 2010 Springer-Verlag.

KW - breast density

KW - computer analysis

KW - human perception

KW - risk assessment

U2 - 10.1007/978-3-642-13666-5_23

DO - 10.1007/978-3-642-13666-5_23

M3 - Article

SN - 0302-9743

VL - 6136

SP - 168

EP - 174

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -

Automated assessment of area of dense tissue in the breast: A comparison with human estimation

Abstract

Keywords

UN SDGs

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