Combined colour deconvolution and artificial intelligence approach for region‐selective immunohistochemical labelling quantification: The example of alpha smooth muscle actin in mouse kidney

Abstract

AbstractImmunohistochemical (IHC) localisation of protein expression is a widely used tool in pathology. This is semi‐quantitative and exhibits substantial intra‐ and inter‐observer variability. Digital approaches based on stain quantification applied to IHC are precise but still operator‐dependent and time‐consuming when regions of interest (ROIs) must be defined to quantify protein expression in a specific tissue area. This study aimed at developing an IHC quantification workflow that benefits from colour deconvolution for stain quantification and artificial intelligence for automatic ROI definition. The method was tested on 10 whole slide images (WSI) of alpha‐smooth muscle actin (aSMA) stained mouse kidney sections. The task was to identify aSMA‐positive areas within the glomeruli automatically. Total aSMA detection was performed using two channels (DAB, haematoxylin) colour deconvolution. Glomeruli segmentation within the same IHC WSI was performed by training a convolutional neural network with annotated examples of glomeruli. For both aSMA and glomeruli, binary masks were created. Co‐localisation was performed by overlaying the masks and assigning red/green colours, with yellow indicative of a co‐localised signal. The workflow described and exemplified using the case of aSMA expression in glomeruli can be applied to quantify the expression of IHC markers within different structures of immunohistochemically stained slides. The technique is objective, has a fully automated threshold approach (colour deconvolution phase) and uses AI to eliminate operator‐dependent steps.