Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms

Susan Astley, Polina Golland (Editor), Nobuhiko Hata (Editor), Christian Barillot (Editor), Joachim Hornegger (Editor), Robert Howe (Editor)

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The radiographic appearance of breast tissue has been established as a strong risk factor for breast cancer. Here we present a complete machine learning framework for automatic estimation of mammographic density (MD) and robust feature extraction for breast cancer risk analysis. Our framework is able to simultaneously classify the breast region, fatty tissue, pectoral muscle, glandular tissue and nipple region. Integral to our method is the extraction of measures of breast density (as the fraction of the breast area occupied by glandular tissue) and mammographic pattern. A novel aspect of the segmentation framework is that a probability map associated with the label mask is provided, which indicates the level of confidence of each pixel being classified as the current label. The Pearson correlation coefficient between the estimated MD value and the ground truth is 0.8012 (p-value
    Original languageEnglish
    Title of host publicationMICCAI 2014: Lecture Notes in Computer Science 8673
    EditorsPolina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe
    PublisherSpringer Nature
    Pages536-543
    Number of pages8
    Publication statusPublished - Sept 2014
    EventMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014 - Boston, USA
    Duration: 14 Sept 201418 Sept 2014

    Conference

    ConferenceMedical Image Computing and Computer-Assisted Intervention – MICCAI 2014
    CityBoston, USA
    Period14/09/1418/09/14

    Keywords

    • Breast Cancer
    • Risk
    • Mammogram
    • Density
    • Segmentation
    • Machine Learning

    Fingerprint

    Dive into the research topics of 'Breast Cancer Risk Analysis Based on a Novel Segmentation Framework for Digital Mammograms'. Together they form a unique fingerprint.

    Cite this