Improving Mammographic Density Estimation in the Breast Periphery

Xin Chen; Emmanouil Moschidis; Christopher Taylor; Susan Astley

doi:10.1007/978-3-319-41546-8_59

Improving Mammographic Density Estimation in the Breast Periphery

Xin Chen, Emmanouil Moschidis, Christopher Taylor, Susan Astley

Division of Informatics, Imaging & Data Sciences (L5)

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

Abstract

Mammographic density is a strong risk factor for breast cancer. Volumetric breast density can be estimated from a digital mammogram by modelling the imaging process; this provides a more accurate assessment than subjective and 2D area-based methods. However, reliable density estimation in the uncompressed peripheral breast region and determination of compression paddle tilt are still open and challenging problems that affect the accuracy of measurement. Here we present a complete system that is able to perform thickness correction for both the compressed and uncompressed breast regions. The system was evaluated on a dataset of 208 mammograms, and compared with results from commercial software VolparaTM (version 1.5). The proposed method yielded Pearson correlation coefficients (PCC) of volumetric breast density (VBD) between left and right breasts of 0.88 (CC view) and 0.91 (MLO view). The PCC between VolparaTM VBD and our method is 0.93.

Original language	English
Title of host publication	Breast imaging
Subtitle of host publication	13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, proceedings
Editors	Anders Tingberg, Kristina Lång, Pontus Timberg
Publisher	Springer Nature
Pages	469-477
ISBN (Print)	978-3-319-41545-1
DOIs	https://doi.org/10.1007/978-3-319-41546-8_59
Publication status	Published - 2016

Publication series

Name	Lecture Notes in Computer Science
Volume	9699

Keywords

Digital mammogram
Volumetric breast density
Thickness correction
Compression paddle tilt

UN SDGs

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

Access to Document

10.1007/978-3-319-41546-8_59

Cite this

Chen, X., Moschidis, E., Taylor, C., & Astley, S. (2016). Improving Mammographic Density Estimation in the Breast Periphery. In A. Tingberg, K. Lång, & P. Timberg (Eds.), Breast imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, proceedings (pp. 469-477). (Lecture Notes in Computer Science; Vol. 9699). Springer Nature. https://doi.org/10.1007/978-3-319-41546-8_59

@inbook{493bf2ea87ce4f1c951c53ef7f15ac41,

title = "Improving Mammographic Density Estimation in the Breast Periphery",

abstract = "Mammographic density is a strong risk factor for breast cancer. Volumetric breast density can be estimated from a digital mammogram by modelling the imaging process; this provides a more accurate assessment than subjective and 2D area-based methods. However, reliable density estimation in the uncompressed peripheral breast region and determination of compression paddle tilt are still open and challenging problems that affect the accuracy of measurement. Here we present a complete system that is able to perform thickness correction for both the compressed and uncompressed breast regions. The system was evaluated on a dataset of 208 mammograms, and compared with results from commercial software VolparaTM (version 1.5). The proposed method yielded Pearson correlation coefficients (PCC) of volumetric breast density (VBD) between left and right breasts of 0.88 (CC view) and 0.91 (MLO view). The PCC between VolparaTM VBD and our method is 0.93.",

keywords = "Digital mammogram, Volumetric breast density, Thickness correction, Compression paddle tilt",

author = "Xin Chen and Emmanouil Moschidis and Christopher Taylor and Susan Astley",

year = "2016",

doi = "10.1007/978-3-319-41546-8_59",

language = "English",

isbn = "978-3-319-41545-1",

series = "Lecture Notes in Computer Science",

publisher = "Springer Nature",

pages = "469--477",

editor = "Tingberg, {Anders } and L{\aa}ng, {Kristina } and Timberg, {Pontus }",

booktitle = "Breast imaging",

address = "United States",

}

Chen, X, Moschidis, E, Taylor, C & Astley, S 2016, Improving Mammographic Density Estimation in the Breast Periphery. in A Tingberg, K Lång & P Timberg (eds), Breast imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, proceedings. Lecture Notes in Computer Science, vol. 9699, Springer Nature, pp. 469-477. https://doi.org/10.1007/978-3-319-41546-8_59

Improving Mammographic Density Estimation in the Breast Periphery. / Chen, Xin; Moschidis, Emmanouil; Taylor, Christopher et al.
Breast imaging: 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, proceedings. ed. / Anders Tingberg; Kristina Lång; Pontus Timberg. Springer Nature, 2016. p. 469-477 (Lecture Notes in Computer Science; Vol. 9699).

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Improving Mammographic Density Estimation in the Breast Periphery

AU - Chen, Xin

AU - Moschidis, Emmanouil

AU - Taylor, Christopher

AU - Astley, Susan

PY - 2016

Y1 - 2016

N2 - Mammographic density is a strong risk factor for breast cancer. Volumetric breast density can be estimated from a digital mammogram by modelling the imaging process; this provides a more accurate assessment than subjective and 2D area-based methods. However, reliable density estimation in the uncompressed peripheral breast region and determination of compression paddle tilt are still open and challenging problems that affect the accuracy of measurement. Here we present a complete system that is able to perform thickness correction for both the compressed and uncompressed breast regions. The system was evaluated on a dataset of 208 mammograms, and compared with results from commercial software VolparaTM (version 1.5). The proposed method yielded Pearson correlation coefficients (PCC) of volumetric breast density (VBD) between left and right breasts of 0.88 (CC view) and 0.91 (MLO view). The PCC between VolparaTM VBD and our method is 0.93.

AB - Mammographic density is a strong risk factor for breast cancer. Volumetric breast density can be estimated from a digital mammogram by modelling the imaging process; this provides a more accurate assessment than subjective and 2D area-based methods. However, reliable density estimation in the uncompressed peripheral breast region and determination of compression paddle tilt are still open and challenging problems that affect the accuracy of measurement. Here we present a complete system that is able to perform thickness correction for both the compressed and uncompressed breast regions. The system was evaluated on a dataset of 208 mammograms, and compared with results from commercial software VolparaTM (version 1.5). The proposed method yielded Pearson correlation coefficients (PCC) of volumetric breast density (VBD) between left and right breasts of 0.88 (CC view) and 0.91 (MLO view). The PCC between VolparaTM VBD and our method is 0.93.

KW - Digital mammogram

KW - Volumetric breast density

KW - Thickness correction

KW - Compression paddle tilt

U2 - 10.1007/978-3-319-41546-8_59

DO - 10.1007/978-3-319-41546-8_59

M3 - Chapter

SN - 978-3-319-41545-1

T3 - Lecture Notes in Computer Science

SP - 469

EP - 477

BT - Breast imaging

A2 - Tingberg, Anders

A2 - Lång, Kristina

A2 - Timberg, Pontus

PB - Springer Nature

ER -

Improving Mammographic Density Estimation in the Breast Periphery

Abstract

Publication series

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this