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

2025-05-08

AI Summary

Alzheimer’s disease (AD) pathological analysis is hindered by the scarcity of annotations for neuritic plaques—structures exhibiting Aβ and tau co-localization—and by staining variability that degrades segmentation performance. To address these challenges, we introduce ADNP-15, the first open-source, whole-slide, fine-grained annotation dataset of human brain tissue images for AD. We propose a novel frequency-domain-based stain normalization and enhancement method that preserves tissue structural details while effectively mitigating inter-slide staining inconsistency. Furthermore, we establish the first dedicated benchmark for neuritic plaque segmentation, systematically evaluating the coupling effects of five segmentation architectures—including U-Net and TransUNet—with four stain normalization techniques, such as Macenko and Vahadane. Our approach achieves an average 4.2% improvement in mDice and substantially enhances model generalizability. All data, annotations, and code are publicly released to support reproducible digital pathology research in AD.

Technology Category

• Computer Vision: Segmentation
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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.

Problem

• Lack of large-scale annotated datasets for neuritic plaque segmentation
• Impact of staining variations on automated image analysis
• Need for effective stain normalization and enhancement techniques

Innovation

• Open-source dataset for neuritic plaque segmentation
• Frequency domain image enhancement technique
• Comprehensive benchmark with deep learning models