Clustering based quantitative breast density assessment using 3D transmission ultrasound

Bilal Malik; Sanghyeb Lee; Rajni Natesan; James Wiskin

doi:10.1117/12.2543069

16 March 2020 Clustering based quantitative breast density assessment using 3D transmission ultrasound

Bilal Malik, Sanghyeb Lee, Rajni Natesan, James Wiskin

Proceedings Volume 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography; 113190H (2020) https://doi.org/10.1117/12.2543069
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

Conference Presentation

Citation Download Citation

Bilal Malik, Sanghyeb Lee, Rajni Natesan, and James Wiskin "Clustering based quantitative breast density assessment using 3D transmission ultrasound", Proc. SPIE 11319, Medical Imaging 2020: Ultrasonic Imaging and Tomography, 113190H (16 March 2020); https://doi.org/10.1117/12.2543069

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