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Proceedings Paper

Morphological segmentation of multiprobe fluorescence images for immunophenotyping in melanoma tissue sections
Author(s): Alasdair I. Dow; Steven A. Shafer; Alan S. Waggoner
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Paper Abstract

A fundamental task in studying the action of cancer chemotherapy is to determine the quantity and spatial relationship of tumor-infiltrating lymphocyte populations. Classically this is performed by staining thin tissue sections with antibodies by immunoperoxidase amplification. The staining technique is practically limited to locating a single cell type per tissue section. Full immunophenotyping requires successive staining of serial sections, using statistical analysis to correlate the results. This paper describes a system that brings together multi- parameter fluorescence imaging and morphological segmentation techniques to provide a fast, accurate, and automatic analysis of the lymphocyte infiltrate in tissue sections. With fluorescence techniques a single section can be stained with up to four distinct fluorescently labelled antibodies to determine cell phenotypes. To harness this potential computer vision techniques are required to analyze the images. A routine based on the water shed algorithm has been developed that segments the nuclei image with an accuracy of greater than 90%. By matching the nuclei boundaries to the local peak fluorescence, cell boundary estimates are obtained in the antigen images. By then extracting two measurements from the boundary signal the cells can be classified according to their antigen expression. Determining cell expression of multiple antigens simultaneously provides a more detailed and accurate picture of the tumor infiltrate than single parameter analysis, and increases understanding of the immune response associated with the chemotherapy.

Paper Details

Date Published: 20 August 1993
PDF: 12 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150163
Show Author Affiliations
Alasdair I. Dow, Carnegie Mellon Univ. (United States)
Steven A. Shafer, Carnegie Mellon Univ. (United States)
Alan S. Waggoner, Carnegie Mellon Univ. (United States)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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