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  Indian J Med Microbiol
 

Figure 2: Pictorial depiction of computational workflow for topometric proliferative hotspot detection and mapping for tumor tissue. (a) Input whole-slide image is overlain with a grid of tiles corresponding to 3000 × 3000 pixels each or approximately ×4 magnification. The processing includes a series of steps to reduce the confounding effects of tissue and staining artifacts [Supplemental Figure S2]. Specific color and shape object features corresponding to red chromogen-immunolabeled proliferating cell nuclei within the image are segmented and quantified according to filters established. (b) Mitotic figure extracted features are binarized, counted, and exported to a spreadsheet, as a rank order set of tiles, based on count magnitude. These mitotic figure values are represented in a slope plot for the specimen for up to the first 100 tiles containing the most mitotic figure. (c) Local tissue invariant feature extraction methods are used to obtain the proper orientation of selected identifying features within grid tiles of interest, for those tiles with the largest counts, tiles h1–h5. These are then marked by a bounding box and displayed on the thumbnail image of the input tissue image

Figure 2: Pictorial depiction of computational workflow for topometric proliferative hotspot detection and mapping for tumor tissue. (a) Input whole-slide image is overlain with a grid of tiles corresponding to 3000 × 3000 pixels each or approximately ×4 magnification. The processing includes a series of steps to reduce the confounding effects of tissue and staining artifacts [Supplemental Figure S2]. Specific color and shape object features corresponding to red chromogen-immunolabeled proliferating cell nuclei within the image are segmented and quantified according to filters established. (b) Mitotic figure extracted features are binarized, counted, and exported to a spreadsheet, as a rank order set of tiles, based on count magnitude. These mitotic figure values are represented in a slope plot for the specimen for up to the first 100 tiles containing the most mitotic figure. (c) Local tissue invariant feature extraction methods are used to obtain the proper orientation of selected identifying features within grid tiles of interest, for those tiles with the largest counts, tiles h1–h5. These are then marked by a bounding box and displayed on the thumbnail image of the input tissue image