Figure 1. Neurofibrillary tangle model annotation and training pipeline.

A) Representative slide with three regions of interest and their point-annotated NFTs. Processing steps are as follows: B) Isolate rotated ROIs and transform global NFT coordinates to local coordinates. C) Zoom to point annotation (right), isolate the DAB channel (middle), and apply morphological operations and Otsu thresholding to segment large “blobs” (left). D) Apply a center bias to remove off-center NFT candidates, retaining a single blob per tile. E) Stitch tiles into a single large ground truth mask for each ROI, replacing point annotations. F) Generate class-balanced batches for training via random NFT and tile sampling from the ROI using an input tile size of 1024×1024 pixels. G) Slice WSIs in a structured format with a stride equal to tile size. H) Feed tiles into UNet with ResNet50 backbone to generate prediction masks. I) Stitch tile predictions and overlay them onto the WSI to visualize the heatmap.