{"id":11,"date":"2025-05-06T21:32:02","date_gmt":"2025-05-06T21:32:02","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/?page_id=11"},"modified":"2025-12-09T16:19:22","modified_gmt":"2025-12-09T16:19:22","slug":"experiments","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/experiments\/","title":{"rendered":"Experiments"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Datasets<\/h2>\n\n\n\n<p>The datasets we use to validate are presented in the table below.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"818\" height=\"218\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-4.png\" alt=\"\" class=\"wp-image-139\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-4.png 818w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-4-300x80.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-4-768x205.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Dice Similarity Coefficient<\/h2>\n\n\n\n<p>For the performance measurement, we use the Dice Similarity Coefficient. Specifically, in our case, we visualize the ground truth, mask prediction, and overlap in three different colors. The yellow part of the image below is the overlap between the ground truth and prediction, the green part is the ground truth only, and the red part is the mask prediction only.  <\/p>\n\n\n\n<p>Below is the formula for the Dice Similarity Coefficient. We can replace X with the prediction mask and Y with the ground truth. The maximum score is 1 for a perfect overlap, whereas the lowest score we can get is 0 for no overlap. <\/p>\n\n\n\n<p><strong>Dice Similarity Coefficient<\/strong> [3] <\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"390\" height=\"129\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/05\/dice_form.png\" alt=\"\" class=\"wp-image-117\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/05\/dice_form.png 390w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/05\/dice_form-300x99.png 300w\" sizes=\"auto, (max-width: 390px) 100vw, 390px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Results<\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"873\" height=\"326\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-3.png\" alt=\"\" class=\"wp-image-138\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-3.png 873w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-3-300x112.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-3-768x287.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"840\" height=\"523\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-2.png\" alt=\"\" class=\"wp-image-136\" srcset=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-2.png 840w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-2-300x187.png 300w, https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-2-768x478.png 768w\" sizes=\"auto, (max-width: 767px) 89vw, (max-width: 1000px) 54vw, (max-width: 1071px) 543px, 580px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Citations<\/h2>\n\n\n\n<p>[1] Orlando, J. I., Fu, H., Breda, J. B., Van Keer, K., Bathula, D. R., Zheng, Y., &#8230; &amp; Trucco, E. (2020). <em>REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs<\/em>. <em>Medical Image Analysis<\/em>, 59, 101570. <a>https:\/\/doi.org\/10.1016\/j.media.2019.101570<\/a> <\/p>\n\n\n\n<p>[2] Ku\u015f, Z., &amp; Aydin, M. (2024). <em>MedSegBench: A comprehensive benchmark for medical image segmentation in diverse data modalities<\/em>. <em>Scientific Data<\/em>, 11, 1283. <a href=\"https:\/\/doi.org\/10.1038\/s41597-024-04159-2\">https:\/\/doi.org\/10.1038\/s41597-024-04159-2<\/a><\/p>\n\n\n\n<p>[3] Swerdlow, M., Guler, \u00d6., Yaakov, R., &amp; Armstrong, D. G. (2023). <em>Simultaneous segmentation and classification of pressure injury image data using Mask-R-CNN<\/em>. <em>Computational and Mathematical Methods in Medicine<\/em>, 2023, Article ID 3858997. <a href=\"https:\/\/doi.org\/10.1155\/2023\/3858997\">https:\/\/doi.org\/10.1155\/2023\/3858997<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Datasets The datasets we use to validate are presented in the table below. Dice Similarity Coefficient For the performance measurement, we use the Dice Similarity Coefficient. Specifically, in our case, we visualize the ground truth, mask prediction, and overlap in three different colors. The yellow part of the image below is the overlap between the &hellip; <\/p>\n<p class=\"link-more\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/experiments\/\" class=\"more-link\">Continue reading<span class=\"screen-reader-text\"> &#8220;Experiments&#8221;<\/span><\/a><\/p>\n","protected":false},"author":246,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-11","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Experiments - Medical Segmentation with Foundation Models: A Prompt-Based, Text-Guided, Training-Free Pipeline<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/experiments\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Experiments - Medical Segmentation with Foundation Models: A Prompt-Based, Text-Guided, Training-Free Pipeline\" \/>\n<meta property=\"og:description\" content=\"Datasets The datasets we use to validate are presented in the table below. Dice Similarity Coefficient For the performance measurement, we use the Dice Similarity Coefficient. Specifically, in our case, we visualize the ground truth, mask prediction, and overlap in three different colors. 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