  Accurate characterization of microcalcifications (MCs) in 2D full-field
digital screening mammography is a necessary step towards reducing diagnostic
uncertainty associated with the callback of women with suspicious MCs.
Quantitative analysis of MCs has the potential to better identify MCs that have
a higher likelihood of corresponding to invasive cancer. However, automated
identification and segmentation of MCs remains a challenging task with high
false positive rates. We present Hessian Difference of Gaussians Regression
(HDoGReg), a two stage multi-scale approach to MC segmentation. Candidate high
optical density objects are first delineated using blob detection and Hessian
analysis. A regression convolutional network, trained to output a function with
higher response near MCs, chooses the objects which constitute actual MCs. The
method is trained and validated on 435 mammograms from two separate datasets.
HDoGReg achieved a mean intersection over the union of 0.670$\pm$0.121 per
image, intersection over the union per MC object of 0.607$\pm$0.250 and true
positive rate of 0.744 at 0.4 false positive detections per $cm^2$. The results
of HDoGReg perform better when compared to state-of-the-art MC segmentation and
detection methods.
