Zhou Xiangrong, Han Mingxu, Hara Takeshi, Fujita Hiroshi, Sugisaki Keiko, Chen Huayue, Lee Gobert, Yokoyama Ryujiro, Kanematsu Masayuki, Hoshi Hiroaki
Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Yanagito 1-1, Gifu, Japan.
Comput Med Imaging Graph. 2008 Dec;32(8):699-709. doi: 10.1016/j.compmedimag.2008.08.007. Epub 2008 Oct 11.
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 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 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. The probability is estimated from the location of the mammary glands and chest muscles in CT images. The location is investigated from a knowledge base that stores pre-recognized anatomical structures using a number of different CT scans. 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 two 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.
