Deep learning chronic wasting disease (CWD) immunohistochemistry (IHC) image dataset

The dataset contains 143 whole-slide images (WSI) containing a combination of central nervous system tissue, typically obex containing the dorsal motor nucleus of the Vagus (DMNV; n= 137) and retropharyngeal lymph nodes (RPLN; n = 114) derived from surveillance diagnostic samples and farmed cervid depopulations. Species represented in the training data set included white tailed deer (n = 68), sheep (n= 54), elk (n = 14), goat (n = 4), and moose (n = 3). Of the 143 slides, 54 were identified as suspect (i.e. detected) and 89 were not detected. Ground truth annotations for lymphoid follicles in retropharyngeal lymph nodes and the dorsal motor nucleus of the Vagus (DMNV) in obex samples were manually annotated by a transmissible spongiform encephalopathy (TSE) trained board-certified veterinary anatomic pathologist. Annotations were performed in QuPath 5.0 using the brush tool. In total, the training data set contains 3,296 annotations broken down into +/- DMNV regions (n = 224+/438-, resp..., Dataset case selection
Formalin-fixed, paraffin-embedded (FFPE) tissues, including retropharyngeal lymph node and obex submitted for TSE surveillance were retrospectively selected from the Washington Animal Disease Diagnostic Laboratory (WADDL) as well as United States Department of Agriculture Agricultural Research Service Animal Disease Research Unit (USDA-ARS-ADRU) scrapie research cases. Inclusion criteria required that cases had been previously evaluated by a TSE trained veterinary pathologist and assigned one of the following diagnostic categories based on immunohistochemistry (IHC): Detected, Not Detected, Location, or Insufficient Follicles. These categories reflect standard interpretive outcomes used in TSE surveillance programs and represent the full spectrum of tissue and staining conditions encountered in diagnostic practice. Cases were excluded if they had been assigned an unacceptable or unsuitable diagnostic code typically due to poor sample fixation and subsequent postmo..., # Deep learning chronic wasting disease (CWD) immunohistochemistry (IHC) image dataset

Dataset DOI: [10.5061/dryad.w6m905r2d](https://doi.org/10.5061/dryad.w6m905r2d)

## Description of the data and file structure

This dataset was created to provide training data for deep learning image analysis approach specifically tailored to review slides from large-scale veterinary prion disease surveillance. The training dataset includes 143 prion IHC whole-slide images (WSI) containing a total of 3,296[ ](#_msocom_1)manual annotations. Annotated images were segmented into non-overlapping tiles and then used to fine-tune a pretrained convolutional neural network, enhancing the model’s ability to recognize prion-specific quality-control parameters and staining features. When tested on a separate, blinded dataset of 50 CWD IHC slides, the model achieved 100% concordance for tissue classification (brain vs. lymph node), 94% concordance for identifying relevant anatomical structures (lymphoid follic...,