Microglial Ground Truth Dataset for StainAI

This dataset is utilized by a deep learning tool, StainAI, that converts immunohistochemistry images into quantitative maps for direct and high-throughput quantification of microglia. The development of StainAI involves a four-step process: (1) image pre-processing, (2) image curation and creation of a ground truth dataset, (3) development of the deep learning system, and (4) morphological mapping and analysis to evaluate microglia in low-magnification (20X) 2D IHC slides. The ground truth datasets include manually labeled microglia for training, testing, and validating the deep learning models for cell detection, segmentation, and classification.Guidelines for cell labeling procedure: StainAI_annotation guidelines.docxDatasheet for annotation guidelines: StainAI_AnnotationDatasheet.xlsxDataset for cell detection by YOLO/MaskR-CNN: train_yolo_maskRCNN.zipDataset for cell segmentation by UNet: train_Unet.zipDataset for cell classification by C50: train_C50.zip<br>