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Fully Automatic and Real-Time Microrobot Detection and Tracking based on Ultrasound Imaging using Deep Learning
Karim Botros, Mohammad Alkhatib, David Folio, Antoine Ferreira

Fully Automatic and Real-Time Microrobot Detection and Tracking based on Ultrasound Imaging using Deep Learning

Science & Mathematics Published 2022 Verification Pending

Overview & Synopsis

Micro-scale robots introduce great prospective into many different medical applications such as targeted drug delivery, minimally invasive surgery and localized bio-metric diagnostics. This research presents a method for object detection and tracking system of a chain-like magnetic microsphere robots using ultrasound imaging in an in-vitro environment. The method estimates the position of the microrobot in realtime using deep learning techniques. The experiments showed that a spherical microrobot with about 500 m in diameter can be detected and tracked in real-time with a high accuracy in dynamic environments. The results exhibit a high detection and tracking accuracy for one, two and three sphere microrobots with the highest accuracy in detection and tracking around 95 % and 93% respectively.

Indexed from repository: HAL Open Science (hal.science)
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Document Information

Document Title Fully Automatic and Real-Time Microrobot Detection and Tracking based on Ultrasound Imaging using Deep Learning
Author / Organization Karim Botros, Mohammad Alkhatib, David Folio, Antoine Ferreira
Subject & Discipline Multidisciplinary Academic Open Research
Categories Science & Mathematics
Publication Year 2022
License / Rights Open Access / Creative Commons
Resource Verification Listed PDF (Verification Pending)
Original Host Website HAL Open Science (hal.science)
File Format application/pdf (Portable Document Format)
Language EN
Catalog ID DOC-240428
Original Source Link Visit Source Page on hal.science »