Automated selection of DAB-labeled tissue for immunohistochemical quantification.

@article{citeulike:2772718, abstract = {The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels.}, address = {Laboratory of Reparative Biology and Bioengineering, Department of Plastic Surgery, University of Texas M. D. Anderson Cancer Center and University of Texas Center for Biomedical Engineering, Houston 77030, USA.}, author = {Brey, E. M. and Lalani, Z. and Johnston, C. and Wong, M. and McIntire, L. V. and Duke, P. J. and Patrick, C. W. }, citeulike-article-id = {2772718}, issn = {0022-1554}, journal = {The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society}, keywords = {imaging, method}, month = {May}, number = {5}, pages = {575--584}, posted-at = {2008-05-08 16:44:17}, priority = {4}, title = {Automated selection of DAB-labeled tissue for immunohistochemical quantification.}, url = {http://www.jhc.org/cgi/content/abstract/51/5/575}, volume = {51}, year = {2003} }

TY - JOUR ID - citeulike:2772718 L3 - citeulike-article-id:2772718 TI - Automated selection of DAB-labeled tissue for immunohistochemical quantification. JF - The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society VL - 51 IS - 5 SP - 575 EP - 584 AD - Laboratory of Reparative Biology and Bioengineering, Department of Plastic Surgery, University of Texas M. D. Anderson Cancer Center and University of Texas Center for Biomedical Engineering, Houston 77030, USA. SN - 0022-1554 N2 - The increased use of immunohistochemistry (IHC) in both clinical and basic research settings has led to the development of techniques for acquiring quantitative information from immunostains. Staining correlates with absolute protein levels and has been investigated as a clinical tool for patient diagnosis and prognosis. For these reasons, automated imaging methods have been developed in an attempt to standardize IHC analysis. We propose a novel imaging technique in which brightfield images of diaminobenzidene (DAB)-labeled antigens are converted to normalized blue images, allowing automated identification of positively stained tissue. A statistical analysis compared our method with seven previously published imaging techniques by measuring each one's agreement with manual analysis by two observers. Eighteen DAB-stained images showing a range of protein levels were used. Accuracy was assessed by calculating the percentage of pixels misclassified using each technique compared with a manual standard. Bland-Altman analysis was then used to show the extent to which misclassification affected staining quantification. Many of the techniques were inconsistent in classifying DAB staining due to background interference, but our method was statistically the most accurate and consistent across all staining levels. KW - imaging KW - method AU - Brey, EM AU - Lalani, Z AU - Johnston, C AU - Wong, M AU - McIntire, LV AU - Duke, PJ AU - Patrick, CW PY - 2003/05// UR - http://www.jhc.org/cgi/content/abstract/51/5/575 ER -