DOI: 10.5061/dryad.w6m905r2d
title: Deep learning chronic wasting disease (CWD) immunohistochemistry (IHC) image dataset

description: 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-,
 respectively) and +/- lymphoid follicular regions (n = 1295+/1339-,
 respectively). The dataset was collected and annotated in order to train
 deep neural networks for tissue type and anatomical structure detection.
 The code is available in an accompanying Github repository.
description: <strong>Dataset case selection</strong>
 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 postmortem autolysis.
 <strong>Tissue processing and
 staining</strong> All immunohistochemical
 staining and evaluation were performed according to the NVSL document:
 Detection of Scrapie and Chronic Wasting Disease by
 Immunohistochemistry.<sup>22</sup> Briefly, all samples were
 formalin fixed in 10% neutral buffered formalin with routine processing
 and embedding. Slides were cut at 4 µm thickness and mounted on NVSL
 approved charged slides. Mounted slides were pretreated with 96% Formic
 Acid for 5 minutes and transferred to Tris Buffer 0.05M, pH 7.5 to rinse.
 Decloaking was performed utilizing the Diva Decloaker solution (Cat#
 DV2004; Biocare Medical, Pacheco, CA) in combination with the BioCare
 antigen retrieval chamber pot (Cat# DCARC0001; Biocare Medical, Pacheco,
 CA). IHC was performed on the Ventana Discovery Ultra
 autostainer (Roche Diagnostics, Indianapolis, IN), employing the
 ready-to-use (RTU) dispensers for F99 monoclonal antibody provided in the
 Anti-Prion Research Kit (Cat# 760-231; Roche Diagnostics, Indianapolis,
 IN). Individual RTUs additionally contain biotinylated secondary antibody,
 an alkaline phosphatase–streptavidin detection system, and a substrate
 chromogen composed of fast red A, naphthol, and fast red B, followed by
 hematoxylin counterstaining. Each staining run included a positive control
 section.
 <strong>Digitization</strong> All
 whole slide images were acquired using a Leica GT450 using default
 settings at a magnification of 400x (image resolution: 0.264 µm/pixel or
 96,154 pixels/inch). All WSI images were saved as .SVS files and viewed or
 annotated using QuPath throughout the project.
description: # 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 follicles and dorsal
 motor nucleus), and 100% concordance for chromogen staining when compared
 to evaluation by a trained veterinary pathologist. ### Files and variables
 The dataset consists of 143 images in SVS format (slide001.svs -
 slide143.svs). These are provided in 30 zip files (images01.zip -
 images30.zip). Slide resolution is in the megapixel range with
 approximately 0.264 micrometers per pixel. Each image consists of several
 structures that are annotated using the QuPath software system for
 bioimage analysis. The annotations were created using the QuPath brush
 tool and consist of either "Follicle",
 "Non-follicular", "Dorsal motor nucleus", "Not
 DMN", "Midline" or "Not midline". The QuPath
 project is included in the qupath.zip file. ## Code/software Software for
 processing the images is available in the GitHub repo
 at [https://github.com/holderlb/Deep-CWD-IHC](https://github.com/holderlb/Deep-CWD-IHC). This repo include code for extracting image annotations, generating image tiles, training deep learning models, and using the deep learning models to analyze new images. See the README file there for details. QuPath version 0.5 ([https://qupath.github.io](https://qupath.github.io)) was used to create the annotations. The included QuPath project (qupath.zip) contains a standard QuPath project file (project.qpproj) and supporting files written by QuPath and includes references to all images and their annotations. To view the images in QuPath, first extract the qupath.zip file and extract the images to some directory. Open QuPath, select File -&gt; Project -&gt; Open Project, and choose the project.qpproj file. Since your images will likely be in a different directory than on our platform, QuPath will complain about not finding the images. Select the Search button in that window, navigate to the directory containing your images, and then select Apply Changes. Double-clicking on an image in the left menu will display the full image with annotations.