Software for Accurate Segmentation of Cell Nuclei in Breast Tissue

Automatic segmentation of cell nuclei is critical in several high-throughput cytometry and pathology applications (1), such as spatial analysis of genetic loci by fluorescence in situ hybridization ("FISH"), whereas manual segmentation is laborious (2). Current automated segmentation methods have varying performance in the presence of distortions introduced during sample preparation, non-uniform illumination, clustering of the individual objects of interest (cells or cell nuclei), and seldom assess boundary accuracy.

Researchers at the National Cancer Institute-Frederick, NIH, have developed an automatic algorithm to segment cell nuclei (3) and FISH signals from two-dimensional images of breast tissue. This automated system integrates a series of advanced image processing methods to overcome the delays inherent to current manual methods for segmenting (delineating) individual cell nuclei in tissue samples. The system automatically selects a subset of nuclei that with high likelihood are accurately segmented. This system has been validated using both simulated and actual datasets that have been accurately analyzed by manual methods. The system generalizes to independent analysis of many spatial parameters useful for studying spatial gene positioning in interphase nuclei, and potentially has a wide range of diagnostic pathology, cytological and high throughput screening applications. CRADA Opportunity: The inventers, working for the Office of the Director, National Cancer Institute, are seeking statements of capability or interest from parties interested in collaborative research (using the Cooperative Research and Development Agreement (CRADA) or Material Transfer Agreement (MTA)) to further develop, evaluate, or commercialize the software for accurate segmentation of cell nuclei and FISH signals in tissue sections. Collaborators working in the field of quantitative and automated pathology may be interested. Please contact John Hewes, Ph.D. at 301-435-3121 or hewesj@mail.nih.gov for more information. Click here to view the NCI collaborative opportunity announcement.

Source: http://www.ott.nih.gov/Technologies/abstractDetails.aspx?RefNo=2137

Amber Valletta America Ferrera Amerie Amy Cobb Amy Smart