Quantifying effect-specific mammographic density

Authors:
Jakob Raundahl;Marco Loog;Paola Pettersen;Mads Nielsen
Affiliations:
University of Copenhagen, Department of Computer Science, Denmark;University of Copenhagen, Department of Computer Science, Denmark and Nordic Bioscience, Herlev, Denmark;Center for Clinical and Basic Research, Ballerup, Denmark;University of Copenhagen, Department of Computer Science, Denmark and Nordic Bioscience, Herlev, Denmark
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

A methodology is introduced for the automated assessment of structural changes of breast tissue in mammograms. It employs a generic machine learning framework and provides objective breast density measures quantifying the specific biological effects of interest. In several illustrative experiments on data from a clinical trial, it is shown that the proposed method can quantify effects caused by hormone replacement therapy (HRT) at least as good as standard methods. Most interestingly, the separation of subpopulations using our approach is considerably better than the best alternative, which is interactive. Moreover, the automated method is capable of detecting age effects where standard methodologies completely fail.