Automated quantification of nuclear immunohistochemical markers with different complexity

Abstract

Manual quantification of immunohistochemically stained nuclear markers is still laborious and subjective and the use of computerized systems for digital image analysis have not yet resolved the problems of nuclear clustering. In this study, we designed a new automatic procedure for quantifying various immunohistochemical nuclear markers with variable clustering complexity. This procedure consisted of two combined macros. The first, developed with a commercial software, enabled the analysis of the digital images using color and morphological segmentation including a masking process. All information extracted with this first macro was automatically exported to an Excel datasheet, where a second macro composed of four different algorithms analyzed all the information and calculated the definitive number of positive nuclei for each image. One hundred and eighteen images with different levels of clustering complexity was analyzed and compared with the manual quantification obtained by a trained observer. Statistical analysis indicated a great reliability (intra-class correlation coefficient > 0.950) and no significant differences between the two methods. Bland-Altman plot and Kaplan-Meier curves indicated that the results of both methods were concordant around 90% of analyzed images. In conclusion, this new automated procedure is an objective, faster and reproducible method that has an excellent level of accuracy, even with digital images with a high complexity.