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He W, Juette A, Denton ER, et al. A Review on Automatic Mammographic Density and Parenchymal Segmentation. Int J Breast Cancer. 2015;2015:276217doi: 10.1155/2015/276217.
He, W., Juette, A., Denton, E. R., Oliver, A., Martí, R., & Zwiggelaar, R. (2015). A Review on Automatic Mammographic Density and Parenchymal Segmentation. International journal of breast cancer, 2015276217. https://doi.org/10.1155/2015/276217
He, Wenda, et al. "A Review on Automatic Mammographic Density and Parenchymal Segmentation." International journal of breast cancer vol. 2015 (2015): 276217. doi: https://doi.org/10.1155/2015/276217
He W, Juette A, Denton ER, Oliver A, Martí R, Zwiggelaar R. A Review on Automatic Mammographic Density and Parenchymal Segmentation. Int J Breast Cancer. 2015;2015:276217. doi: 10.1155/2015/276217. Epub 2015 Jun 11. PMID: 26171249; PMCID: PMC4481086.
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Int J Breast Cancer. 2015;2015:276217. doi: 10.1155/2015/276217. Epub 2015 Jun 11.

A Review on Automatic Mammographic Density and Parenchymal Segmentation.

International journal of breast cancer

Wenda He, Arne Juette, Erika R E Denton, Arnau Oliver, Robert Martí, Reyer Zwiggelaar

Affiliations

  1. Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.
  2. Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK.
  3. Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain.

PMID: 26171249 PMCID: PMC4481086 DOI: 10.1155/2015/276217

Abstract

Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.

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