A Review on Automatic Mammographic Density and Parenchymal Segmentation

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.

Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice

“…Our results provide further evidence of the need for objective, standardized measures of breast density. A number of automated software programs have been developed for density quantification (27); these provide highly reproducible (28), objective measures of density typically on a continuous scale from 0–100%. Further research is needed to examine whether such automated tools can identify women who would benefit from supplemental breast cancer screening in addition to mammography.…”

Classification of mammographic microcalcification clusters with machine learning confidence levels

“…Nowadays, computer-aided diagnosis/detection (CAD) systems are being widely used in hospitals to speed up diagnosis. In breast mammography, CAD can be used to estimate breast density, [1][2][3] measuring the characteristics of breast mass, 4,5 modelling the distribution of microcalcification cluster, 6 for risk assessment 7 and as a preprocessing method. 8 Breast CAD systems in clinical environments are seen as an invaluable tool and are often used as a 'second reader' opinion.…”

Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment

“…The calculation of the projected 2-D area of dense tissue requires a segmentation of the dense tissue areas in the image. 8 For quantification of the 3-D volume of the dense tissue, the physics of the image acquisition process is modeled and it can involve either a precalibration of the system, 9 a calibration object in the acquired image, 10 or an image-based self-calibration step. 11 A breast density (percentage) value can be computed by dividing the area or volume of dense tissue with the total area or volume of the breast.…”