Table 6: Summary of representative studies using 2D projection based volumetric methods for mammographic tissue segmentation. denotes correlation coefficient. Note that in case of multiple results, only the best reported results are listed.

Study Year Number of density categories Modalities Number of views Number of images Segmentation evaluation Risk/density estimation accuracy

Prior calibration
Heine et al. [79] 2011 Dense and fatty FFDM (raw) CC 106 cases and 106 controlsVisually assessed; linear = 0.78 (automatic-Cumulus) Risk estimates associated with the lowest to highest quartiles, odds ratios: 1.0, 3.4, 3.6, and 5.6

In-image reference phantom based calibration
Alonzo-Proulx et al. [80] 2012 Dense and fatty FFDM CC 55087 Pearson (left, right volumetric density-breast volume) = 0.92, 0.91 N/A
Ourselin et al. [81]
2014 Dense and fatty FFDM CC 480 Percent breast fibroglandular volume, = 0.8 (automatic-predicted) N/A

Physical image formation model
Hartman et al. [82] 2008 Dense and fatty FFDM and MRI MLO and CC 550 (275 pairs) and 88 MRIBreast density volumes, Pearson (left-right breast, Quantra-MRI) = 0.923, 0.937 N/A
Highnam et al. [83] 2010 Dense and fatty FFDM and MRI MLO and CC 2217 and MRI from 26 younger womenBreast density volumes, Pearson (left-right breast, CC-MLO view, Volpara-MRI) = 0.923, 0.915, 0.94 N/A
Gubern-Mérida et al. [84] 2014 Dense and fatty FFDM and MRI MLO and CC 680 and 168 MRI Pearson (volumetric breast density Volpara-MRI, fibroglandular tissue volume Volpara-MRI) = 0.91, 0.84Density grade, = 0.40 (Volpara-BI-RADS)