Open AccessAutomatic detection of cotton balls during brain surgery: where deep learning meets ultrasound imaging to tackle foreign objects
Author(s) -
Smruti Mahapatra,
Manish Balamurugan,
Kathryn Chung,
Venkat Kuppoor,
Eli Curry,
Fariba Aghabaglou,
Tarana Parvez Kaovasia,
Molly Acord,
Ana Ainechi,
Jeong Hun Kim,
Yohannes Tsehay,
Christina Diana Ghinda,
Jennifer K. Son,
Aliaksei Pustavoitau,
Betty Tyler,
Shenandoah Robinson,
Nicholas Theodore,
Henry Brem,
Judy Huang,
Amir Manbachi
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2580887
Subject(s) - computer science , artificial intelligence , ball (mathematics) , ultrasound , object detection , deep learning , computer vision , biomedical engineering , pattern recognition (psychology) , acoustics , medicine , physics , mathematics , mathematical analysis
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.