Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas

X. Zhou; M. Kan; T. Hara; H. Fujita; K. Sugisaki; R. Yokoyama; G. Lee; H. Hoshi

doi:10.1117/12.709345

26 March 2007 Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas

X. Zhou, M. Kan, T. Hara, H. Fujita, K. Sugisaki, R. Yokoyama, G. Lee, H. Hoshi

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65123O (2007) https://doi.org/10.1117/12.709345
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

The identification of mammary gland regions is a necessary processing step during the anatomical structure recognition of human body and can be expected to provide the useful information for breast tumor diagnosis. This paper proposes a fully-automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This scheme calculates the probability for each voxel belonging to the mammary gland or other regions (for example pectoralis major muscles) in CT images and decides the mammary gland regions automatically. The probability is estimated from the location of the mammary gland and pectoralis major muscles in CT images. The location (named as a probabilistic atlas) is investigated from the pre-segmentation results in a number of different CT scans and the CT number distribution is approximated using a Gaussian function. We applied this scheme to 66 patient cases (female, age: 40-80) and evaluated the accuracy by using the coincidence rate between the segmented result and gold standard that is generated manually by a radiologist for each CT case. The mean value of the coincidence rate was 0.82 with the standard deviation of 0.09 for 66 CT cases.

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X. Zhou, M. Kan, T. Hara, H. Fujita, K. Sugisaki, R. Yokoyama, G. Lee, and H. Hoshi "Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123O (26 March 2007); https://doi.org/10.1117/12.709345

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