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Content-Based Retrieval of Similar Radiological Images. Background. Already petabytes (10 15 bytes) of images in the world Yet no means to search for and compare new images to others for which more is known (e.g., diagnosis, survival, coexisting disease,…
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Content-Based Retrieval of Similar Radiological Images Background • Already petabytes (1015 bytes) of images in the world • Yet no means to search for and compare new images to others for which more is known (e.g., • diagnosis, survival, coexisting disease,… • molecular subtype from biopsy or excision • successful therapies
Content-Based Retrieval of Similar Radiological Images What we are doing? • Building system to characterize and store images • Designing methods to compute image similarity • Testing concept in retrieval of CT images of liver lesions
Content-Based Retrieval of Similar Radiological Images How we are doing it • Interface for recording consistent radiologist observations • Software for computer-characterization • Database for storing images and characterizations
Content-Based Retrieval of Similar Radiological Images Pilot Study: Database • 81 portal venous phase liver CTs • 25 cysts, 24 metastases, 14 hemangiomas, 7 HCCs, 6 FNHs, 3 abscesses, 1 laceration, 1 fat deposit
Content-Based Retrieval of Similar Radiological Images Results: Example Query Image 11 Most Similar 12 Least Similar
Content-Based Retrieval of Similar Radiological Images Results: Over all Query Images NDCG Average Performance > 0.9 for 2 or more images: Outstanding! no. of images retrieved
Content-Based Retrieval of Similar Radiological Images Where are we going? Compared to a database containing images and results of gene expression analyses: 75% chance of having these genes overexpressed. Molecular imaging test X can verify expression levels. Drug-Z has been associated with an 85% success rate in patients with these genes overexpressed CAD, 3D, Quantitation + Advanced Image Analysis Decision-supported PACS