弱监督学习自动识别帕金森病脑组织中α-突触核蛋白聚集体形态
Weakly Supervised Segmentation and Classification of Alpha-Synuclein Aggregates in Brightfield Midbrain Images
- 基于弱监督分割与ResNet50分类，处理染色差异
- 对路易小体和神经纤维的分类平衡准确率达80%
- 适合神经病理学研究者分析蛋白聚集体空间分布
帕金森病（PD）是一种神经退行性疾病，其特征是错误折叠的α-突触核蛋白聚集形成路易小体和神经纤维，用于病理诊断。本研究开发了一种自动化图像处理流程，基于弱监督分割方法，在帕金森病及偶然性路易小体病（iLBD）患者中脑组织全幻灯片图像（WSIs）上实现对这些聚集体的分割与分类，使用ResNet50分类器，对免疫组化染色变异具有鲁棒性。该方法可有效区分主要聚集体形态，包括路易小体和神经纤维，平衡准确率达80%。该框架为在明场免疫组化组织中大规模表征α-突触核蛋白聚集体的空间分布与异质性提供了可能，并有助于探究其与周围细胞（如小胶质细胞和星形胶质细胞）之间尚未明确的关系。
原文摘要 · Abstract (English)
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.
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