Paper Abstract and Keywords |
Presentation |
2009-01-20 14:30
[Poster Presentation]
Automated segmentation and analysis of mammary gland regions in CT images using probabilistic atlas Mingxu Han, Xiangrong Zhou, Takeshi Hara, Hiroshi Fujita, Keiko Sugisaki, Huayue Chen, Ryujiro Yokoyama, Masayuki Kanematsu, Hiroaki Hoshi (GifuUniv.) MI2008-154 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The identification of mammary gland regions can provide useful information for breast tumor diagnosis. This study proposes a fully-automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This scheme calculates the probability of each voxel belonging to the mammary gland or chest muscle in CT images as the reference of the segmentation, and decides the mammary gland regions based on CT number automatically. We applied this scheme to 66 patient cases (female, age: 20-80) and evaluated the accuracy by using the Jaccard similarity coefficient (JSC) between the segmented results and 2 gold standards that were generated manually by 2 medical experts independently for each CT case. The result showed that the mean value of the JSC score was 0.83 with the standard deviation of 0.09 for 66 CT cases. The proposed scheme was applied to investigate the breast density distributions in normal mammary gland regions so as to demonstrate the effect and usefulness of the proposed scheme. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
segmentation / mammary gland region / X-ray CT image / probabilistic atlas / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 108, no. 385, MI2008-154, pp. 435-438, Jan. 2009. |
Paper # |
MI2008-154 |
Date of Issue |
2009-01-12 (MI) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
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MI2008-154 |
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