Weakly Supervised Segmentation and Classification of Alpha-Synuclein Aggregates in Brightfield Midbrain Images

TL;DR

This paper presents a weakly supervised deep learning pipeline for automated segmentation and classification of alpha-synuclein aggregates in midbrain images, aiding Parkinson's disease pathology analysis.

Contribution

It introduces a robust, weakly supervised method using ResNet50 for differentiating aggregate morphologies in whole-slide images, addressing variability in immunohistochemical labeling.

Findings

Achieved 80% balanced accuracy in classifying Lewy bodies and neurites.

Enabled large-scale spatial analysis of alpha-synuclein aggregates.

Provided a tool for studying aggregate heterogeneity and relationships with surrounding cells.

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder associated with the accumulation of misfolded alpha-synuclein aggregates, forming Lewy bodies and neuritic shape used for pathology diagnostics. Automatic analysis of immunohistochemistry histopathological images with Deep Learning provides a promising tool for better understanding the spatial organization of these aggregates. In this study, we develop an automated image processing pipeline to segment and classify these aggregates in whole-slide images (WSIs) of midbrain tissue from PD and incidental Lewy Body Disease (iLBD) cases based on weakly supervised segmentation, robust to immunohistochemical labelling variability, with a ResNet50 classifier. Our approach allows to differentiate between major aggregate morphologies, including Lewy bodies and neurites with a balanced accuracy of $80\%$. This framework paves the way for large-scale characterization of the spatial distribution and heterogeneity of alpha-synuclein aggregates in brightfield immunohistochemical tissue, and for investigating their poorly understood relationships with surrounding cells such as microglia and astrocytes.