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A Review on Automatic Mammographic Density and Parenchymal Segmentation.

He W, Juette A, Denton ER, Oliver A, Martí R, Zwiggelaar R - Int J Breast Cancer (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.

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.

No MeSH data available.


Related in: MedlinePlus

Distribution of various mammographic tissue segmentation approaches from 1992 to 2014.
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fig7: Distribution of various mammographic tissue segmentation approaches from 1992 to 2014.

Mentions: This review covers mammographic tissue segmentation existing in the literature covering the period from 1992 to 2014, focusing on automatic approaches, and excluding manual and semiautomatic approaches. Only a fully automatic approach can deliver consistent, standardised, fully reproducible, and comparable measurements for high-throughput breast screening programmes across sites. Figure 7 shows the distribution of various mammographic tissue segmentation approaches, and Figure 8 shows the trend lines for area and volumetric based mammographic density/parenchymal segmentation approaches. This indicates a decline in the number of studies using 2D projection and an increase in the number of studies in 3D volumetric segmentation. Without automation and accurate estimation of breast density/parenchymal patterns, mammographic segmentation developed in a research environment is unlikely to become useful in clinical practices for assessing mammographic risk or in clinical trials evaluating the influence of different variables (drugs) on breast tissue [187]. Eng et al. [10] concluded that fully automated methods are valid alternatives to the labour intensive “gold standard” Cumulus for quantifying density. However, the choice of a particular method will depend on the aims and settings, and it is essential that the same density assessments approach is required for a research design or survey in which the same subjects are observed repeatedly over a period of time. Despite various methodology issues related to the developed techniques as reviewed in Section 4, there are several other obstacles for most of the developed approaches to demonstrate that image-based risk prediction can be incorporated in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models).


A Review on Automatic Mammographic Density and Parenchymal Segmentation.

He W, Juette A, Denton ER, Oliver A, Martí R, Zwiggelaar R - Int J Breast Cancer (2015)

Distribution of various mammographic tissue segmentation approaches from 1992 to 2014.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4481086&req=5

fig7: Distribution of various mammographic tissue segmentation approaches from 1992 to 2014.
Mentions: This review covers mammographic tissue segmentation existing in the literature covering the period from 1992 to 2014, focusing on automatic approaches, and excluding manual and semiautomatic approaches. Only a fully automatic approach can deliver consistent, standardised, fully reproducible, and comparable measurements for high-throughput breast screening programmes across sites. Figure 7 shows the distribution of various mammographic tissue segmentation approaches, and Figure 8 shows the trend lines for area and volumetric based mammographic density/parenchymal segmentation approaches. This indicates a decline in the number of studies using 2D projection and an increase in the number of studies in 3D volumetric segmentation. Without automation and accurate estimation of breast density/parenchymal patterns, mammographic segmentation developed in a research environment is unlikely to become useful in clinical practices for assessing mammographic risk or in clinical trials evaluating the influence of different variables (drugs) on breast tissue [187]. Eng et al. [10] concluded that fully automated methods are valid alternatives to the labour intensive “gold standard” Cumulus for quantifying density. However, the choice of a particular method will depend on the aims and settings, and it is essential that the same density assessments approach is required for a research design or survey in which the same subjects are observed repeatedly over a period of time. Despite various methodology issues related to the developed techniques as reviewed in Section 4, there are several other obstacles for most of the developed approaches to demonstrate that image-based risk prediction can be incorporated in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK.

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.

No MeSH data available.


Related in: MedlinePlus