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Automated mammographic breast density estimation using a fully convolutional network.

Juhun Lee1, Robert M Nishikawa1

  • 1Department of Radiology, University of Pittsburgh, 3362 Fifth Ave.,, Pittsburgh, PA, 15213, USA.

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A new deep learning algorithm accurately estimates mammographic breast density, correlating well with radiologist assessments and outperforming existing methods for improved breast cancer screening.

Keywords:
breast densitydeep learningmammographysegmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Mammographic breast density is a key indicator for breast cancer risk.
  • Accurate breast density estimation is crucial for risk assessment and screening.
  • Current methods for breast density estimation can be subjective and time-consuming.

Purpose of the Study:

  • To develop a fully automated deep learning algorithm for mammographic breast density estimation.
  • To segment breast and fibroglandular tissue accurately using deep learning.
  • To compute breast percent density (PD) automatically.

Main Methods:

  • Utilized a fully convolutional network for segmentation of breast and dense fibroglandular areas.
  • Trained the network on a dataset of 604 full-field digital screening mammograms (MLO and CC views).
  • Validated the algorithm against radiologist BI-RADS density assessments and compared it with the LIBRA algorithm.

Main Results:

  • The algorithm demonstrated strong correlation with radiologist BI-RADS density ratings (Pearson's rho up to 0.85).
  • Outperformed the state-of-the-art LIBRA algorithm in correlation and classification of density categories.
  • Provided reliable and consistent PD estimates across different views and breasts.

Conclusions:

  • A novel deep learning algorithm for automated breast density segmentation and estimation has been developed.
  • The algorithm shows high accuracy and outperforms existing methods.
  • This automated approach has the potential to improve the efficiency and consistency of breast density assessment in clinical practice.