Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by amyloid-beta plaques and tau neurofibrillary tangles, which serve as key histopathological features. The identification and segmentation of these lesions are crucial for understanding AD progression but remain challenging due to the lack of large-scale annotated datasets and the impact of staining variations on automated image analysis. Deep learning has emerged as a powerful tool for pathology image segmentation; however, model performance is significantly influenced by variations in staining characteristics, necessitating effective stain normalization and enhancement techniques. In this study, we address these challenges by introducing an open-source dataset (ADNP-15) of neuritic plaques (i.e., amyloid deposits combined with a crown of dystrophic tau-positive neurites) in human brain whole slide images. We establish a comprehensive benchmark by evaluating five widely adopted deep learning models across four stain normalization techniques, providing deeper insights into their influence on neuritic plaque segmentation. Additionally, we propose a novel image enhancement method that improves segmentation accuracy, particularly in complex tissue structures, by enhancing structural details and mitigating staining inconsistencies. Our experimental results demonstrate that this enhancement strategy significantly boosts model generalization and segmentation accuracy. All datasets and code are open-source, ensuring transparency and reproducibility while enabling further advancements in the field.
Abstract (translated)
阿尔茨海默病（AD）是一种神经退行性疾病，其特征是β淀粉样蛋白斑块和tau神经原纤维缠结的存在。这些病变被认为是该疾病的标志性病理学特征，在理解AD进展中具有关键作用。然而，由于缺乏大规模标注数据集以及染色变化对自动化图像分析的影响，识别和分割这些病变仍然极具挑战性。 深度学习技术因其在病理性图像分割中的强大能力而崭露头角，但模型性能受到染色特性变异的显著影响，这需要有效的染色校准与增强技术。在这项研究中，我们通过引入一个开放源代码数据集（ADNP-15），解决了这些问题，该数据集包含了人类大脑全切片图像中的神经原纤维缠结斑块（即淀粉样蛋白沉积物伴有变性tau阳性的神经原结构冠）。我们在四种染色校准技术上评估了五种广泛采用的深度学习模型，建立了全面的基准测试，从而更深入地了解这些技术对神经原纤维缠结斑块分割的影响。此外，我们提出了一种新的图像增强方法，通过提升复杂组织结构中的细节和减少染色不一致性来提高分割准确性。 我们的实验结果表明，该增强策略显著提高了模型的泛化能力以及分割精度。所有数据集及代码均开放源码以确保透明度与可重复性，并促进相关领域进一步的发展。
URL
https://arxiv.org/abs/2505.05041
