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