{"id":9784,"date":"2024-10-10T13:30:15","date_gmt":"2024-10-10T10:30:15","guid":{"rendered":"https:\/\/aire-edih.eu\/?post_type=project&p=9784"},"modified":"2025-11-03T10:43:50","modified_gmt":"2025-11-03T08:43:50","slug":"2024-co-vision-testing-of-co-registration-of-the-scans-lesion-tracking-and-cancer-dynamics-assessment","status":"publish","type":"project","link":"https:\/\/aire-edih.eu\/en\/project\/2024-co-vision-testing-of-co-registration-of-the-scans-lesion-tracking-and-cancer-dynamics-assessment\/","title":{"rendered":"2024: CO-VISION – testing of Co-registration of the scans lesion tracking and cancer dynamics assessment"},"content":{"rendered":"

The CO-VISION project addressed the challenge of comparing multiple CT scans taken from the same patient over <\/span>time<\/span> which is a time-consuming task for radiologists. The project aimed to <\/span>validate<\/span> the use of AI for automating image co-registration, enabling more <\/span>accurate<\/span> and efficient tracking of <\/span>tumour<\/span> progression. We implemented and tested three co-registration approaches: <\/span>CorrField<\/span>, a classical algorithm; a U-Net-based model trained on <\/span>CorrField<\/span> outputs; and a GAN-based model (Vox2Vox) that learns deformation fields directly. To benchmark performance, we adjusted a synthetic data generation pipeline that simulates longitudinal scan pairs across different organs. While initial testing was conducted on lung data, we extended the solution to kidney scans, <\/span>demonstrating<\/span> its scalability and cross-organ applicability. The project confirmed that AI-based co-registration is both technically <\/span>feasible<\/span> and clinically valuable, laying the groundwork for broader adoption <\/span>in<\/span> radiological workflows.<\/span><\/span>\u00a0<\/span><\/p>\n

Github<\/span><\/a><\/div>\n","protected":false},"excerpt":{"rendered":"

The CO-VISION project addressed the challenge of comparing multiple CT scans taken from the same patient over time which is a time-consuming task for radiologists. The project aimed to validate […]<\/p>\n","protected":false},"featured_media":10674,"template":"","year_tax":[86],"company_tax":[177],"status_tax":[92],"university_tax":[91],"sector":[],"project_cat":[65],"class_list":["post-9784","project","type-project","status-publish","has-post-thumbnail","hentry","year_tax-2024-en","company_tax-bettermedicine-ou-en","status_tax-completed-projects","university_tax-university-of-tartu","project_cat-completed-projects"],"acf":[],"yoast_head":"\nAIRE - 2024: CO-VISION - testing of Co-registration of the scans lesion tracking and cancer dynamics assessment<\/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:\/\/aire-edih.eu\/en\/project\/2024-co-vision-testing-of-co-registration-of-the-scans-lesion-tracking-and-cancer-dynamics-assessment\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AIRE - 2024: CO-VISION - testing of Co-registration of the scans lesion tracking and cancer dynamics assessment\" \/>\n<meta property=\"og:description\" content=\"The CO-VISION project addressed the challenge of comparing multiple CT scans taken from the same patient over time which is a time-consuming task for radiologists. 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