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Proceedings Paper

Clustering based quantitative breast density assessment using 3D transmission ultrasound
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Paper Abstract

Breast density is now recognized as one of the most important independent risk factors of breast cancer. Current means to assess breast density primarily utilize mammograms which represent a series of projection images, making it difficult to estimate the true volume of the fibroglandular tissue. We present 3D transmission ultrasound as a method to visualize and differentiate fibroglandular tissue within the breast and use an unsupervised learning-based method to quantitatively assess the respective breast density. The method includes initial separation of breast from the surrounding water bath followed by segmentation of the whole breast into fibroglandular tissue and fat using fuzzy C-mean (FCM) classification. We apply these methods to both tissue phantoms (in vitro) and clinical breast images (in vivo). In the case of tissue phantoms, the agreement between the theoretical (geometric density) and experimentally calculated values was better than 90%. For density calculation in a sample size of 50 cases, the results correlate well (Spearman r = 0.93, 95% CI: 0.88-0.96, p<0.0001) with an FDA-cleared breast density assessment software, VolparaDensity. We also discuss the advantage of using FCMbased tissue classification over threshold-based tissue segmentation within the paradigm of iterative image inversion/reconstruction and show that the former method is less sensitive to variation in assessment of breast density as a function of iteration count and thus, less dependent on convergence criteria. These results imply that breast density as assessed by 3D transmission ultra-sound can be of significant clinical utility and play an important role in breast cancer risk assessment.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 113190H (16 March 2020); doi: 10.1117/12.2543069
Show Author Affiliations
Bilal Malik, QT Ultrasound LLC (United States)
Sanghyeb Lee, QT Ultrasound LLC (United States)
Rajni Natesan, QT Ultrasound LLC (United States)
Univ. of Texas MD Anderson Cancer Ctr. (United States)
James Wiskin, QT Ultrasound LLC (United States)

Published in SPIE Proceedings Vol. 11319:
Medical Imaging 2020: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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