Development of an unsupervised pixel-based clustering algorithm for compartmentalization of immunohistochemical expression using Automated QUantitative Analysis.

Inherent to most tissue image analysis routines are user-defined steps whereby specific pixel intensity thresholds must be set manually to differentiate background from signal-specific pixels within multiple images. To reduce operator time, remove operator-to-operator variability, and to obtain objective and optimal pixel separation for each image, we have developed an unsupervised pixel-based clustering algorithm allowing for the objective and unsupervised differentiation of signal from background, and differentiation of compartment-specific pixels on an image-by-image basis. We used the Automated QUantitative Analysis (AQUA) platform, a well-established automated fluorescence-based immunohistochemistry image analysis platform used for quantification of protein expression in specific cellular compartments to demonstrate utility of this methodology.