High-throughput analysis of multispectral images of breast cancer tissue

被引:45
|
作者
Adiga, Umesh [1 ]
Malladi, Ravikanth
Fernandez-Gonzalez, Rodrigo
de Solorzano, Carlos Ortiz
机构
[1] Univ Calif Berkeley, Lawrence Berkeley Lab, Dept Math, Berkeley, CA 94720 USA
[2] Univ Calif Berkeley, Lawrence Berkeley Lab, Div Life Sci, Berkeley, CA 94720 USA
[3] Univ Calif Berkeley, Lawrence Berkeley Lab, Joint Grad Grp Bioengn, Berkeley, CA 94720 USA
[4] Univ Calif San Francisco, San Francisco, CA 94143 USA
关键词
cell; coherence; diffusion; segmentation; tissue;
D O I
10.1109/TIP.2006.875205
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Statistical analysis of genetic changes within cell nuclei that are far from the primary tumor would help determine whether such changes have occurred prior to tumor invasion. To determine whether the gene amplification in cells is morphologically and/or genetically related to the primary tumor requires quantitative evaluation of a large number of cell nuclei from continuous meaningful structures such as milk-ducts, tumors, etc., located relatively far from the primary tumor. To address this issue, we have designed an integrated image analysis software system for high-throughput segmentation of nuclei. Filters such as Beltrami flow-based reaction-diffusion, directional diffusion, etc., were used to pre-process the images resulting in a better segmentation. The accurate shape of the segmented nucleus was recovered using an iterative "shrink-wrap" operation. The study of two cases of ductal carcinoma in situ in breast tissue supports the biological observation regarding the existence of a preferential intraductal invasion, and therefore a common origin, between the primary tumor and the gene amplification in the cell-nuclei lining the ductal structures in the breast.
引用
收藏
页码:2259 / 2268
页数:10
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