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.

翻译：帕金森病（PD）是一种神经退行性疾病，与错误折叠的α-突触核蛋白聚集体的积累相关，这些聚集体形成用于病理诊断的路易小体和神经突形态。利用深度学习对免疫组织化学组织病理学图像进行自动分析，为更好地理解这些聚集体的空间组织提供了有前景的工具。在本研究中，我们开发了一种自动化图像处理流程，基于弱监督分割方法，在帕金森病和偶发性路易体病（iLBD）病例的中脑组织全玻片图像（WSIs）中分割和分类这些聚集体，该方法对免疫组织化学标记的变异性具有鲁棒性，并采用ResNet50分类器。我们的方法能够区分主要的聚集体形态，包括路易小体和神经突，其平衡准确率达到$80\\%$。该框架为大规模表征亮视野免疫组织化学组织中α-突触核蛋白聚集体的空间分布和异质性，以及研究其与周围细胞（如小胶质细胞和星形胶质细胞）之间尚不明确的关系奠定了基础。