Improving the Quantitative Analysis of Breast Microcalcifications: A Multiscale Approach.

ABSTRACT: Accurate characterization of microcalcifications (MCs) in 2D digital mammography is a necessary step toward reducing the diagnostic uncertainty associated with the callback of indeterminate MCs. Quantitative analysis of MCs can better identify MCs with a higher likelihood of ductal carcinoma in situ or invasive cancer. However, automated identification and segmentation of MCs remain challenging with high false positive rates. We present a two-stage multiscale approach to MC segmentation in 2D full-field digital mammograms (FFDMs) and diagnostic magnification views. Candidate objects are first delineated using blob detection and Hessian analysis. A regression convolutional network, trained to output a function with a higher response near MCs, chooses the objects which constitute actual MCs.

SUBMITTER: Marasinou C 

PROVIDER: S-EPMC10287598 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature