arXiv 2505.050412025-05-08eess.IVcs.CV

ADNP-15：一个用于人脑全切片图像中神经炎性斑块分割的开源组织病理学数据集，采用频域图像增强进行染色归一化

ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization

School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所-巴黎脑研究所-ICM）；Inserm（法国国家医学院）；CNRS（法国国家科学研究中心）；APHP（法国国家卫生与医学研究院）；Hôpital de la Pitié Salpêtrière（皮蒂埃萨尔佩特里医院）；DMU Neuroscience（DMU神经科学）；Inria（法国国家信息与自动化技术研究院）

Chenxi Zhao, Jianqiang Li, Qing Zhao, Jing Bai, Susana Boluda, Benoit Delatour, Lev Stimmer, Daniel Racoceanu, Gabriel Jimenez, Guanghui Fu

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AI总结：

本文提出开源数据集ADNP-15用于人脑全切片图像中神经炎性斑块分割，并引入频域图像增强方法改善染色归一化，通过多模型多染色归一化基准实验证明该方法显著提升分割精度与泛化能力。

摘要：

阿尔茨海默病（AD）是一种以淀粉样β斑块和tau神经原纤维缠结为特征的神经退行性疾病，这些病变是关键的组织病理学特征。识别和分割这些病变对于理解AD进展至关重要，但由于缺乏大规模标注数据集以及染色差异对自动化图像分析的影响，这一任务仍然具有挑战性。深度学习已成为病理图像分割的有力工具；然而，模型性能受染色特征差异的显著影响，因此需要有效的染色归一化和增强技术。在本研究中，我们通过引入一个开源数据集（ADNP-15）来应对这些挑战，该数据集包含人脑全切片图像中的神经炎性斑块（即淀粉样沉积物与营养不良的tau阳性神经突起冠的组合）。我们通过评估五种广泛采用的深度学习模型在四种染色归一化技术下的表现，建立了一个全面的基准，从而更深入地了解这些技术对神经炎性斑块分割的影响。此外，我们提出了一种新颖的图像增强方法，通过增强结构细节和减轻染色不一致性来提高分割精度，尤其是在复杂组织结构中。我们的实验结果表明，这种增强策略显著提升了模型的泛化能力和分割精度。所有数据集和代码均为开源，确保了透明性和可复现性，同时推动该领域的进一步发展。

英文摘要：

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.

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