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-, 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.