Alzheimer’s disease (AD), one of the most common cause of dementia, is a complex neurodegenerative disease marked by amyloid-β (Aβ) plaques and hyperphosphorylated tau tangles. Genome-wide association studies have identified rare variants of genes that implicate novel biological underpinnings of AD, unearthing untapped insights into modulation of innate immune pathways. Recent studies have implicated crucial functions of microglia (brain’s resident immune cells) clustering around Aβ plaques, such as plaque compaction and containment, suggesting a beneficial impact on limiting the extent of neuronal damage. In order to test this hypothesis, extraction of neuronal damage characteristics in correlation with microglia coverage is required on a single plaque level. We utilized immunohistochemistry and confocal microscopy to collect 3D image data sets from an AD mouse model. For the quantitative correlative assessment of the heterogeneity of microglia clustering and plaque-associated neuronal damage, we developed a multi-step image analysis pipeline consisting of (a) U-Net based automated region of interest (ROI) detection algorithm (96 % true positive rate), (b) FIJI-based custom-built image profiling tool that creates biologically meaningful image features from ROIs (plaques), and (c) Spotfire-based data visualization dashboard. Our proof-of-concept data set shows that plaque-associated microglia clustering correlates with lower neuronal damage in a disease stage and plaque size-dependent manner. This novel platform has validated our working hypothesis on protective functions of microglia during AD pathology. Future applications of the plaque profiling pipeline will enable unbiased quantitative assessment of potential neuroprotective effects by pharmacological or genetic interventions in preclinical AD models with amyloid pathology.