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Proceedings Paper

Automatic quantification of mammary glands on non-contrast x-ray CT by using a novel segmentation approach
Author(s): Xiangrong Zhou; Takuya Kano; Yunliang Cai; Shuo Li; Xinxin Zhou; Takeshi Hara; Ryujiro Yokoyama; Hiroshi Fujita
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Paper Abstract

This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851Z (24 March 2016); doi: 10.1117/12.2217256
Show Author Affiliations
Xiangrong Zhou, Gifu Univ. School of Medicine (Japan)
Takuya Kano, Gifu Univ. School of Medicine (Japan)
Yunliang Cai, Western Univ. (Canada)
Shuo Li, Western Univ. (Canada)
Xinxin Zhou, Nagoya Bunri Univ. (Japan)
Takeshi Hara, Gifu Univ. School of Medicine (Japan)
Ryujiro Yokoyama, Gifu Univ. School of Medicine (Japan)
Hiroshi Fujita, Gifu Univ. School of Medicine (Japan)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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