Characterizing ultraound images through breast density related features using traditional and deep learning approaches

Type: BA thesis

Status: finished

Date: October 29, 2020 - March 29, 2021

Supervisors: Andreas Maier, Dalia Rodriguez Salas

Breast cancer is the most common cancer in women worldwide with accounting for almost a quarter of all new female cancer cases [ 1 ]. In order to improve the chances of recovery and reduce mortality, it is crucial to detect and diagnose it as early as possible. Mammography is the standard treatment when screening for breast cancer. While mammography images have an important role in cancer diagnosis, it has been shown that the sensitivity of these images decreases with a high mammographic density [2].The mammographic density (MD) refers to the amount of fibroglandular tissue in the breast in proportion to the amount of fatty tissue. MD is an established risk factor for breast cancer. The general risk for developing breast cancer is increased with a higher density. Women with a density of 25% or higher are twice as likely to develop breast cancer, with 75% even five times, compared to women with a MD of less than 5% [3]. In addition there is a possibility that in dense breast a tumor may be masked on a mammogram [2]. Therefore it is necessary to consider the breast’s density when screening for breast cancer. Several studies aimed at supporting and improving breast cancer diagnosis with computer aided systems and feature evaluation and such studies have taken the MD into consideration when evaluating mammography images [4][5].
In order to detect those tumors that are masked on mammography or support inconclusive findings, often an additional ultrasound (US) is conducted on women with high MD [6]. However US images underlie a high inter-observer variability. Computer-aided diagnosis aims to develop a method to analyze US images and support diagnosis with the aim of reducing this variability. The approach of this thesfa is to transfer and ad just the methods designed by Häberle et al. [4] used for characterizing 2-D mammographic images in order to use them on 3-D ultrasound images while only focusing on features correlating with the MD.
Additionally, more features will be generated using deep leaming, as most of the recent computer-aided diagnosis tools do not rely on traditional methods anymore. Over the last years deep learning has become the standard when working in medical imaging and several studies have shown a promising perf ormance when working with breast ultrasound images [7][8].
U sing both traditional and deep leaming methods for extracting features aims to improve the classification of possibly cancerous tissue by building a reliable set of features which characterize the MD of the patient. Furthermore, the traditional features may help to interpret those generated through deep learning approaches, in turn, the latter may help to show the benefit of using deep leaming when analyzing medical images.
This thesis will cover the following points:

• Literature review of mammographic density as a risk factor for breast cancer and ultrasound as an additional screening method
• Extraction and evaluation of a variety of automated features in ultrasound images using traditional and deep leaming approaches
• Analyzing the relationship of the extracted features with the mammographic density


] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: a cancer joumal for clinicians, vol. 68, no.6,pp.394-424,2018.[2] P. E. Freer, “Mammographie breast density: impact on breast cancer risk and implications for screening,” Radiographics : a review publication of the Radiological Society of NorthAmerica, Inc, vol. 35, no. 2, pp. 302-315, 2015.
[3] V. A. McCormack, “Breast density and parenchymal pattems as markers of breast cancer risk: A meta-analysis,” Cancer Epidemiology and Prevention Biomarkers, vol. 15, no. 6, pp. 1159-1169, 2006.
[4] L. Häberle, F. Wagner, P. A. Fasching, S. M. Jud, K. Heusinger, C. R. Loehberg, A. Hein, C. M. Bayer, C. C. Hack, M. P. Lux, K. Binder, M. Elter, C. Milnzenmayer, R. Schulz-Wendtland, M. Meier-Meitinger, B. R. Adamietz, M. Uder, M. W. Beckmann, and T. Wittenberg, “Characterizing mammographic images by using generic texture features,” Breast cancer research: BCR, vol. 14, no. 2, 2012.
[5] M. Tan, F. Aghaei, Y. Wang, and B. Zheng, “Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions,” Physics in medicine and biology, vol. 62, no. 2, pp.358-376,2017.
[6] L. Häberle, C. C. Hack, K. Heusinger, F. Wagner, S. M. Jud, M. Uder, M. W. Beckmann, R. Schulz-Wendtland, T. Wittenberg, and P. A. Fasching, “Using automated texture features to determine the probability for masking ofa tumor on mammography, but not ultrasound,” Europeanjoumal of medical research, vol. 22, no. 1, 2017.
[7] H. Tanaka, S.-W. Chiu, T. Watanabe, S. Kaoku, and T. Yamaguchi, “Computer-aided diagnosis system for breast ultrasound images using deep Ieaming,” Physics in medicine and biology, vol. 64, no. 23, 2019.
[8] M. H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, A. K. Davison, R. Marti, and H. Y. Moi, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEEjoumal of biomedical and health informatics, vol.22,no.4,pp. 1218-1226,2018.