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Journal of Biomedical Optics

Automatic segmentation of histological structures in mammary gland tissue sections
Author(s): Rodrigo Fernandez-Gonzalez; Thomas Deschamps; Adam Idica; Ravikanth Malladi; Carlos Ortiz de Solorzano
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Paper Abstract

Real-time three-dimensional (3-D) reconstruction of epithelial structures in human mammary gland tissue blocks mapped with selected markers would be an extremely helpful tool for diagnosing breast cancer and planning treatment. Besides its clear clinical application, this tool could also shed a great deal of light on the molecular basis of the initiation and progression of breast cancer. We present a framework for real-time segmentation of epithelial structures in two-dimensional (2-D) images of sections of normal and neoplastic mammary gland tissue blocks. Complete 3-D rendering of the tissue can then be done by surface rendering of the structures detected in consecutive sections of the blocks. Paraffin-embedded or frozen tissue blocks are first sliced and sections are stained with hematoxylin and eosin. The sections are then imaged using conventional bright-field microscopy and their background corrected using a phantom image. We then use the fast-marching algorithm to roughly extract the contours of the different morphological structures in the images. The result is then refined with the level-set method, which converges to an accurate (subpixel) solution for the segmentation problem. Finally, our system stacks together the 2-D results obtained in order to reconstruct a 3-D representation of the entire tissue block under study. Our method is illustrated with results from the segmentation of human and mouse mammary gland tissue samples.

Paper Details

Date Published: 1 May 2004
PDF: 10 pages
J. Biomed. Opt. 9(3) doi: 10.1117/1.1699011
Published in: Journal of Biomedical Optics Volume 9, Issue 3
Show Author Affiliations
Rodrigo Fernandez-Gonzalez, Lawrence Berkeley National Lab. (United States)
Thomas Deschamps, Lawrence Berkeley National Lab. (United States)
Adam Idica, Lawrence Berkeley National Lab. (United States)
Ravikanth Malladi, Lawrence Berkeley National Lab. (United States)
Carlos Ortiz de Solorzano, Lawrence Berkeley National Lab. (United States)

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