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Automatic atlas-based segmentation of the breast in MRI for 3D breast volume computation.

Abstract

PURPOSE
Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Prior breast segmentation is required when quantifying breast density with MRI in order to calculate the total breast volume and exclude nonbreast surrounding tissues. This paper describes an automatic 3D breast volume segmentation approach.
METHODS
The method is based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume. The boundary localization framework is integrated to a subsequent shape atlas-based segmentation using a Laplacian framework.
RESULTS
The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm, respectively, and an average volume error of 153.8 cm(3) when tested on 409 MRI datasets. Furthermore, the breast volume assessment technique will produce a 5.3% variability in the estimation of breast density in the tested population.
CONCLUSIONS
The fully automated segmentation approach of the breast in MRI allows the computation of total breast volume, a step required for breast density assessment. The use of features invariant to image intensity and a shape atlas to reinforce shape consistency are attractive characteristics of the method. Error analysis demonstrates that 5.3% variability in the estimation of breast density incurred by the method is an acceptable trade-off.

Links

  • Publisher Full Text
  • Authors

    Ortiz CG, Martel AL

    Source

    Medical physics 39:10 2012 Oct pg 5835-48

    MeSH

    Adolescent
    Adult
    Atlases as Topic
    Automation
    Breast
    Breast Neoplasms
    Female
    Humans
    Imaging, Three-Dimensional
    Magnetic Resonance Imaging
    Middle Aged
    Models, Anatomic
    Poisson Distribution
    Young Adult

    Pub Type(s)

    Journal Article
    Research Support, Non-U.S. Gov't

    Language

    eng

    PubMed ID

    23039622