Generalized plaque digitization framework for multi-dimensional mesoscopic images

Abstract

Fine analysis of the spatial distribution and morphology of Aβ plaques is crucial for understanding the pathological progression of Alzheimer's disease (AD). However, at the whole-brain scale, the enormous number of plaques, wide size range, diffuse morphologies, and complex imaging background pose challenges that existing digitization methods often fail to address comprehensively. To this end, this study developed a Generalized Plaque Digitization Framework (GPDigit). The framework adopts a two-step strategy of detection followed by segmentation: first, a customized object detection network achieves precise spatial localization of plaques; second, adaptive foreground signal segmentation is performed within local regions. This design circumvents the difficulty of global threshold selection and the high annotation cost of segmentation networks. GPDigit supports both 2D and 3D data scenarios, ensures detection accuracy through targeted feature extraction mechanisms, and significantly improves training data preparation efficiency with a self-developed annotation tool and multiple data augmentation strategies. Experimental results demonstrate that GPDigit achieves satisfactory digitization performance under challenging conditions such as dense plaque distribution, severe background interference, and weak signals. Application to whole-brain plaque analysis in 5xFAD mice across multiple key ages revealed, at the fine brain-region scale, the spatiotemporal heterogeneity of plaque density and load development. This study provides a systematic solution for plaque digitization in neuropathological mesoscopic high-resolution images and offers a practical tool to advance AD pathology research.