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

Xin Chen, Emmanouil Moschidis, Christopher Taylor, Susan Astley

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-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<0.0001). We demonstrate the capability of our methods to discriminate between women with and without cancer by analyzing the contralateral mammograms of 50 women with unilateral breast cancer, and 50 controls. Using MD we obtained an area under the ROC curve (AUC) of 0.61; however our texture-based measure of mammographic pattern significantly outperforms the MD discrimination with an AUC of 0.70.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2014
Subtitle of host publication17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I
EditorsPolina Golland, Nobuhiko Hata, Christian Barillot, Joachim Hornegger, Robert Howe
PublisherSpringer Nature
Pages536-543
Volume8673
ISBN (Print)978-3-319-10403-4
DOIs
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science

Keywords

  • Digital mammogram
  • segmentation
  • breast cancer risk
  • mammographic density
  • texture analysis

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