{"id":4730,"date":"2025-11-12T12:26:21","date_gmt":"2025-11-12T11:26:21","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=4730"},"modified":"2026-01-27T13:19:28","modified_gmt":"2026-01-27T12:19:28","slug":"divide-by-roi","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/divide-by-roi\/","title":{"rendered":"Analyzing and Comparing Regions of Interest \u2013 \u201cDivide by ROIs\u201d Special"},"content":{"rendered":"\n

What makes MIKAIA\u00ae<\/sup><\/a> a powerful tool for image analysis is its ability to compute statistics and make comparisons not only between scans, but also within a scan between different regions of interests (ROIs). One key feature, the \u201cDivide by ROIs\u201d option, available in MIKAIA\u2019s analysis apps, allows users to aggregate statistics and gain a clearer understanding of how cell populations differ across various regions of interest. The concept is very generic, but concrete examples include:<\/p>\n\n\n\n

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  1. Count positive cells both inside a metastasis and in the surrounding microenvironment<\/li>\n\n\n\n
  2. Calculate immune cell density in the metastasis core, across three bands of inwards and outwards concentric margins, and the remaining area outside of the tumor.<\/li>\n\n\n\n
  3. Analyze IHC staining across TMA cores.<\/li>\n<\/ol>\n\n\n\n

    If you\u2019re using MIKAIA\u00ae <\/sup>for the first time or if you haven\u2019t explored all its features yet, you might find that the Divide by ROIs option requires a little extra guidance. That\u2019s exactly what this MIKAIA\u00ae<\/sup> University app note<\/a> aims to do: break down the terminology and guide you through the necessary steps to make comparisons within a slide\u2019s image.<\/p>\n\n\n\n\n\n\n

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    Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n