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Advanced Radiological Image Retrieval System for Enhanced Diagnosis

Developing a content-based retrieval system for radiological images to facilitate comparison and analysis for improved diagnosis and treatment planning. Our innovative approach involves building a robust database, designing similarity computation methods, and conducting pilot studies on liver lesions. By leveraging advanced technology, we aim to enhance medical image analysis and decision-making processes.

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Advanced Radiological Image Retrieval System for Enhanced Diagnosis

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  1. 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

  2. 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

  3. 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

  4. 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

  5. Content-Based Retrieval of Similar Radiological Images Results: Example Query Image 11 Most Similar 12 Least Similar

  6. 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

  7. 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

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