Estimating Cell Count and Distribution in Labeled Histological Samples Using Incremental Cell Search
Author(s) -
Oscar Meruvia-Pastor,
Jung Soh,
Eric J. Schmidt,
Julia C. Boughner,
Mei Xiao,
Heather A. Jamniczky,
Benedikt Hallgrímsson,
Christoph W. Sensen
Publication year - 2011
Publication title -
international journal of biomedical imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.626
H-Index - 41
eISSN - 1687-4196
pISSN - 1687-4188
DOI - 10.1155/2011/874702
Subject(s) - embryonic stem cell , cell , stain , computer science , cell size , sampling (signal processing) , process (computing) , cell growth , cell counting , biology , computational biology , microbiology and biotechnology , computer vision , staining , genetics , cell cycle , gene , filter (signal processing) , operating system
Cell proliferation is critical to the outgrowth of biological structures including the face and limbs. This cellular process has traditionally been studied via sequential histological sampling of these tissues. The length and tedium of traditional sampling is a major impediment to analyzing the large datasets required to accurately model cellular processes. Computerized cell localization and quantification is critical for high-throughput morphometric analysis of developing embryonic tissues. We have developed the Incremental Cell Search (ICS), a novel software tool that expedites the analysis of relationships between morphological outgrowth and cell proliferation in embryonic tissues. Based on an estimated average cell size and stain color, ICS rapidly indicates the approximate location and amount of cells in histological images of labeled embryonic tissue and provides estimates of cell counts in regions with saturated fluorescence and blurred cell boundaries. This capacity opens the door to high-throughput 3D and 4D quantitative analyses of developmental patterns.
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