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Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping.

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    IEEE Transactions on Medical Imaging
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    A new dataset, Surgical Tattoos in Infrared (STIR), uses invisible IR-fluorescent dye for precise tissue tracking in endoscopic surgery. This method overcomes limitations of existing datasets, enabling better analysis of surgical navigation technologies.

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    Area of Science:

    • Medical Imaging
    • Surgical Technology
    • Computer Vision

    Background:

    • Accurate tissue tracking and mapping are crucial for image-guided surgery and automation.
    • Existing datasets for endoscopic environments have limitations, including rigid setups, visible markers, or costly, error-prone manual annotation.
    • There is a need for robust datasets that facilitate the quantitative evaluation of tracking and mapping methods in realistic surgical scenarios.

    Purpose of the Study:

    • Introduce a novel dataset, Surgical Tattoos in Infrared (STIR), for evaluating endoscopic tissue tracking and mapping methods.
    • Provide a dataset with persistent, yet invisible to visible spectrum algorithms, labels for improved accuracy and generalizability.
    • Enable quantitative analysis and benchmarking of various tracking and mapping algorithms in diverse surgical contexts.

    Main Methods:

    • Developed a novel labeling methodology using indocyanine green (ICG), an IR-fluorescent dye, to create persistent labels on tissue.
    • Collected hundreds of stereo video clips (in vivo and ex vivo) using visible light cameras, with labels marked in the IR spectrum.
    • The STIR dataset comprises over 3,000 labeled points, offering a comprehensive resource for algorithm evaluation.

    Main Results:

    • The STIR dataset was successfully created, featuring labels invisible to visible light algorithms but detectable in the IR spectrum.
    • Multiple frame-based tracking methods were analyzed using the STIR dataset.
    • Performance was evaluated using both 3D and 2D endpoint error and accuracy metrics, providing quantitative insights into algorithm performance.

    Conclusions:

    • The STIR dataset provides a valuable resource for quantifying the performance of tissue tracking and mapping methods in endoscopic settings.
    • The novel labeling approach overcomes limitations of previous datasets, offering improved robustness and generalizability.
    • STIR facilitates advancements in image guidance and automation for medical interventions and surgery.