Fig. 4: Region Annotation, Cell Segmentation and Classification. | Nature Communications

Fig. 4: Region Annotation, Cell Segmentation and Classification.

From: Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging

Fig. 4

a Binary pixel classifiers were trained for multiple spatial regions (as indicated) in QuPath on a subset of image data. Tissue deformations and imaging errors were removed from regions of interest (ROIs) during quality control checks (white arrow indicates a representative example of a tissue tear). Scale bar = 500 µm. b Nuclei in murine glioblastoma samples were manually annotated to train StarDist nuclear detection models. Detection accuracy was compared to threshold-based watershed segmentation and the publicly available StarDist model for immunofluorescence (DSB_Heavy). Segmented nuclei were expanded by 2.5 µm to capture cell cytoplasm and classified by semi-supervised cell classification. Scale bars = 50 µm. Region annotation, cell segmentation, and cell classification were applied independently to all (c) PDGfp and (d) BrM HIFI images to generate fully annotated digital pathology images. Scale bars = 400 µm.

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