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    Automatic detection of cotton balls during brain surgery: Where deep learning meets ultrasound imaging to tackle foreign objects.

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    Author
    Mahapatra, Smruti
    Balamurugan, Manish
    Chung, Kathryn
    Kuppoor, Venkat
    Curry, Eli
    Aghabaglau, Fariba
    Kaovasia, Tarana Parvez
    Acord, Molly
    Ainechi, Ana
    Kim, Jeong Hun
    Tshey, Yohannes
    Ghinda, Christina Diana
    Son, Jennifer K
    Pustavoitau, Aliaksei
    Tyler, Betty
    Robinson, Shenandoah D
    Theodore, Nicholas
    Brem, Henry
    Huang, Judy
    Manbachi, Amir
    Show allShow less

    Date
    2021-02-26
    Journal
    Proceedings of SPIE--the International Society for Optical Engineering
    Publisher
    SPIE, The International Society for Optical Engineering
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1117/12.2580887
    https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/35233128/
    Abstract
    Cotton balls are a versatile and efficient tool commonly used in neurosurgical procedures to absorb fluids and manipulate delicate tissues. However, the use of cotton balls is accompanied by the risk of accidental retention in the brain after surgery. Retained cotton balls can lead to dangerous immune responses and potential complications, such as adhesions and textilomas. In a previous study, we showed that ultrasound can be safely used to detect cotton balls in the operating area due to the distinct acoustic properties of cotton compared with the acoustic properties of surrounding tissue. In this study, we enhance the experimental setup using a 3D-printed custom depth box and a Butterfly IQ handheld ultrasound probe. Cotton balls were placed in variety of positions to evaluate size and depth detectability limits. Recorded images were then analyzed using a novel algorithm that implements recently released YOLOv4, a state-of-the-art, real-time object recognition system. As per the radiologists' opinion, the algorithm was able to detect the cotton ball correctly 61% of the time, at approximately 32 FPS. The algorithm could accurately detect cotton balls up to 5mm in diameter, which corresponds to the size of surgical balls used by neurosurgeons, making the algorithm a promising candidate for regular intraoperative use.
    Keyword
    Deep learning
    neuroimaging
    object detection
    recognition system
    retained foreign object
    ultrasound
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/18154
    ae974a485f413a2113503eed53cd6c53
    10.1117/12.2580887
    Scopus Count
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