Fully automated breast density assessment from low-dose chest CT

Shuang Liu, Laurie R. Margolies, Yiting Xie, David F. Yankelevitz, Claudia I. Henschke, Anthony P. Reeves

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

6 Scopus citations

Abstract

Breast cancer is the most common cancer diagnosed among US women and the second leading cause of cancer death 1. Breast density is an independent risk factor for breast cancer and more than 25 states mandate its reporting to patients as part of the lay mammogram report 2. Recent publications have demonstrated that breast density measured from low-dose chest CT (LDCT) correlates well with that measured from mammograms and MRIs 3-4, thereby providing valuable information for many women who have undergone LDCT but not recent mammograms. A fully automated framework for breast density assessment from LDCT is presented in this paper. The whole breast region is first segmented using an anatomy-orientated novel approach based on the propagation of muscle fronts for separating the fibroglandular tissue from the underlying muscles. The fibroglandular tissue regions are then identified from the segmented whole breast and the percentage density is calculated based on the volume ratio of the fibroglandular tissue to the local whole breast region. The breast region segmentation framework was validated with 1270 LDCT scans, with 96.1% satisfactory outcomes based on visual inspection. The density assessment was evaluated by comparing with BI-RADS density grades established by an experienced radiologist in 100 randomly selected LDCT scans of female subjects. The continuous breast density measurement was shown to be consistent with the reference subjective grading, with the Spearman's rank correlation 0.91 (p-value < 0.001). After converting the continuous density to categorical grades, the automated density assessment was congruous with the radiologist's reading in 91% cases.

Original languageEnglish
Title of host publicationMedical Imaging 2017
Subtitle of host publicationComputer-Aided Diagnosis
EditorsNicholas A. Petrick, Samuel G. Armato
PublisherSPIE
ISBN (Electronic)9781510607132
DOIs
StatePublished - 2017
EventMedical Imaging 2017: Computer-Aided Diagnosis - Orlando, United States
Duration: 13 Feb 201716 Feb 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10134
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2017: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityOrlando
Period13/02/1716/02/17

Keywords

  • Breast cancer
  • Breast density assessment
  • Fully automated
  • Low-dose chest CT

Fingerprint

Dive into the research topics of 'Fully automated breast density assessment from low-dose chest CT'. Together they form a unique fingerprint.

Cite this