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
Journal:
arXiv
Published Date:
May 8, 2025
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.
