Translator Disclaimer
Paper
1 April 2016 Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer
Author Affiliations +
Abstract
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuxin Wang, Peng Gu, Won-Mean Lee, Marilyn A. Roubidoux, Sidan Du, Jie Yuan, Xueding Wang, and Paul L. Carson "Automated 3D ultrasound image segmentation for assistant diagnosis of breast cancer", Proc. SPIE 9790, Medical Imaging 2016: Ultrasonic Imaging and Tomography, 979011 (1 April 2016); https://doi.org/10.1117/12.2203245
PROCEEDINGS
8 PAGES


SHARE
Advertisement
Advertisement
Back to Top