{"id":2867,"date":"2024-12-10T12:49:01","date_gmt":"2024-12-10T11:49:01","guid":{"rendered":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/?p=2867"},"modified":"2025-10-29T21:55:50","modified_gmt":"2025-10-29T20:55:50","slug":"grid-analysis-app","status":"publish","type":"post","link":"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/grid-analysis-app\/","title":{"rendered":"Quantify Histological Growth Pattern or Spatial Heterogeneity with the Grid Analysis App"},"content":{"rendered":"\n<p>After cells or tissue regions have been identified with any of the various apps available in <a href=\"http:\/\/www.mikaia.ai\">MIKAIA<sup>\u00ae<\/sup><\/a>, it is oftentime of interest to analyze their spatial distribution. The Grid Analysis App offers valuable insights on spatial heterogeneity or histological growth patterns.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Are cells evenly spread out accross the tissue or do they cluster in specific places? <\/li>\n\n\n\n<li>Does tumor slowly replace the surrounding stroma by pushing it away or does it infiltrate the stroma and show heavy budding? <\/li>\n<\/ul>\n\n\n\n<p>Such questions can be quantified by using the Grid Analysis App. The Grid Analysis App overlays a regular grid on top of the tissue area (or user-selected subregion). The tile size can be specified. For each tile, a metric is then computed. Available metrics are: <\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<ul class=\"wp-block-list\">\n<li>measurements on a single set of classes:\n<ul class=\"wp-block-list\">\n<li><strong>Absolute amount<\/strong><br>Example: count number of tumor cells in each tile<\/li>\n\n\n\n<li><strong>Area [mm\u00b2]<\/strong><br>Example: tumor area in each tile.<\/li>\n\n\n\n<li><strong>Area [%]<\/strong><br>Example: percentage of tumor area from entire tisssue (foreground) area in each tile<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>measurements for investigating relations between\n<ul class=\"wp-block-list\">\n<li><strong>Ratio #A \/ #B<\/strong><br>Example: ratio of tumor to immune cells in each tile<\/li>\n\n\n\n<li><strong>Ratio area(A) \/ area(B)<\/strong><br>Example: ratio of tumor to inflamed area in each tile<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"166\" height=\"125\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image.png\" alt=\"\" class=\"wp-image-2871\" style=\"width:151px;height:auto\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image.png 166w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-80x60.png 80w\" sizes=\"(max-width: 166px) 100vw, 166px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Tumor growth pattern<\/h2>\n\n\n\n<p>Here, we analyze an IHC tumor marker. The first step is to mask the tumor and stroma areas. We use the <strong>Mask by Color App<\/strong> (other app note: <a href=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/perineural-invasion\/\">MIKAIA<sup>\u00ae<\/sup>: Quantifying Perineural Invasion in Duplex IHC<\/a>) and configure it to perform H-DAB unmixing and then threshold the DAB channel. Here, we do not analyze the entire slide, but constrain the analysis to a manually outlined region of interest (pink):<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"540\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-1024x540.jpg\" alt=\"IHC tumor marker\" class=\"wp-image-2868\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-1024x540.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-300x158.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-768x405.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-370x195.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-270x142.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-570x301.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1-740x390.jpg 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild1.jpg 1280w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>The Mask by Color App generates a tumor mask (orange) as well as a stroma mask (blue).<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"526\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-1024x526.jpg\" alt=\"MIKAIA Mask by Color App result: masks of tumor and stroma\" class=\"wp-image-2869\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-1024x526.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-300x154.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-768x394.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-370x190.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-270x139.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-585x300.jpg 585w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-570x293.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2-740x380.jpg 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild2.jpg 1280w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Grid Analysis App<\/strong><\/h2>\n\n\n\n<p>Next, we select the <strong>Grid Analysis App<\/strong> from the App Center and configure it. We select the metric &#8220;<strong>Area [%]<\/strong>&#8221; and select our &#8220;Tumor&#8221; class. We use a tile size of 100&#215;100 \u00b5m and set up 10 grades. <\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"483\" height=\"488\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-4.png\" alt=\"MIKAIA Grid Analysis App configuration panel 1\" class=\"wp-image-2875\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-4.png 483w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-4-297x300.png 297w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-4-370x374.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-4-270x273.png 270w\" sizes=\"(max-width: 483px) 100vw, 483px\" \/><\/figure>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"473\" height=\"512\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-3.png\" alt=\"MIKAIA Grid Analysis App configuration panel 2\" class=\"wp-image-2874\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-3.png 473w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-3-277x300.png 277w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-3-370x401.png 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/image-3-270x292.png 270w\" sizes=\"(max-width: 473px) 100vw, 473px\" \/><\/figure>\n<\/div>\n<\/div>\n\n\n\n<p>The analysis only takes a few second. Tiles are added to the scene as individual rectangles and assigned to the class based on their grade. The class colors are based on a heatmap (blue for lower grades to red for higher grades). <\/p>\n\n\n<div id='gallery-1' class='gallery galleryid-2867 gallery-columns-3 gallery-size-gridlove-single'><figure class='gallery-item'>\n\t\t\t<div class='gallery-icon landscape'>\n\t\t\t\t<a class=\"gridlove-popup\" href='https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3.jpg'><img decoding=\"async\" width=\"740\" height=\"384\" src=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-740x384.jpg\" class=\"attachment-gridlove-single size-gridlove-single\" alt=\"\" srcset=\"https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-740x384.jpg 740w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-300x156.jpg 300w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-1024x531.jpg 1024w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-768x399.jpg 768w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-370x192.jpg 370w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-270x140.jpg 270w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3-570x296.jpg 570w, https:\/\/websites.fraunhofer.de\/smart-sensing-insights\/wp-content\/uploads\/2024\/12\/Bild3.jpg 1289w\" sizes=\"(max-width: 740px) 100vw, 740px\" \/><\/a>\n\t\t\t<\/div><\/figure>\n\t\t<\/div>\n\n\n\n<p>A histogram of the tiles is automatically computed using 10 bins. All values, including the histogram, are exportable to a CSV spreadsheet, which also contains the individual metric values per tile. It can be seen that tumors with a pushing border have either tiles with close to 100% tumor or close to 0% tumor, while infiltrating tumors have many tiles that contain both tumor and stroma.  <\/p>\n","protected":false},"excerpt":{"rendered":"<p>Result of Grid Analysis App shows histological growth pattern and spatial heterogeneity<\/p>\n","protected":false},"author":2,"featured_media":2870,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,35],"tags":[87,7,29,109],"coauthors":[56],"class_list":["post-2867","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-pathology","category-life-science","tag-ihc","tag-mikaia","tag-mikaia-app-note","tag-use-case"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>MIKAIA\u00ae Grid Analysis App: Analyze Spatial Heterogeneity<\/title>\n<meta name=\"description\" content=\"The Grid Analysis App offers valuable insights on spatial heterogeneity or histological growth patterns. 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