Improved digital breast tomosynthesis images using automated ultrasound

Zhang, Xing; Yuan, Jie; Du, Sidan; Kripfgans, Oliver D.; Wang, Xueding; Carson, Paul L.; Liu, Xiaojun

doi:10.1118/1.4875980

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…14 With technological advances, further developments in DBT and its possible synergistic combination with other modalities such as ultrasonography (USG) or optical imaging of the breasts will continue to evolve. [15][16][17]…”

Section: T H E R E a R E C O N C E R N S A B O U T T H E D E T E C T mentioning

confidence: 99%

Recent Advances and Application of Breast Imaging in Clinical Practice

Wl¹,

Sitt²,

Fung³

et al. 2015

Hong Kong J Radiol

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Breast imaging is an essential cornerstone in the management of breast cancer. It serves not only as breast cancer screening, but also in diagnosis, treatment, and follow-up of patients with breast disease. Attempts to enhance the current imaging modalities and development of new technology are underway. We provide a short overview of the existing imaging modalities including mammography, tomosynthesis, ultrasound and magnetic resonance imaging, and recent technological advances including positron emission mammography, contrast digital mammography, elastography, three-dimensional / contrast-enhanced ultrasound and automated wholebreast ultrasound screening. Magnetic resonance spectroscopy, new state-of-the-art applications in optical imaging, cone-beam computed tomography, magnetic resonance-ultrasound navigation, and molecular imaging techniques are also briefly discussed.

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Section: T H E R E a R E C O N C E R N S A B O U T T H E D E T E C T mentioning

confidence: 99%

Recent Advances and Application of Breast Imaging in Clinical Practice

Wl¹,

Sitt²,

Fung³

et al. 2015

Hong Kong J Radiol

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“…For the purposes of improving breast ultrasound imaging, segmentation of all tissues is important since different tissue properties affect the propagation of ultrasound and electromagnetic waves. Hooi et al (2014) [34] and Zhang et al (2014) [35] both applied image correction on the reconstruction of phantom ultrasound volumes by inclusion of a priori information. These limited-angle or -aperture reconstructions require a priori information such as boundaries of major tissues, which can be obtained by all-tissue segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automated 3D ultrasound image segmentation to aid breast cancer image interpretation

Lee

Roubidoux

et al. 2016

Ultrasonics

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Segmentation of an ultrasound image into functional tissues is of great importance to clinical diagnosis of breast cancer. However, many studies are found to segment only the mass of interest and not all major tissues. Differences and inconsistencies in ultrasound interpretation call for an automated segmentation method to make results operator-independent. Furthermore, manual segmentation of entire three-dimensional (3D) ultrasound volumes is time-consuming, resource-intensive, and clinically impractical. Here, we propose an automated algorithm to segment 3D ultrasound volumes into three major tissue types: cyst/mass, fatty tissue, and fibro-glandular tissue. To test its efficacy and consistency, the proposed automated method was employed on a database of 21 cases of whole breast ultrasound. Experimental results show that our proposed method not only distinguishes fat and non-fat tissues correctly, but performs well in classifying cyst/mass. Comparison of density assessment between the automated method and manual segmentation demonstrates good consistency with an accuracy of 85.7%. Quantitative comparison of corresponding tissue volumes, which uses overlap ratio, gives an average similarity of 74.54%, consistent with values seen in MRI brain segmentations. Thus, our proposed method exhibits great potential as an automated approach to segment 3D whole breast ultrasound volumes into functionally distinct tissues that may help to correct ultrasound speed of sound aberrations and assist in density based prognosis of breast cancer.

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