USMicroMagSet: Using Deep Learning Analysis to Benchmark the Performance of Microrobots in Ultrasound Images

@article{botross2023,
  author = {Botross, Karim and Mohammad, Alkhatib and Folio, David and
    Ferreira, Antoine},
  publisher = {IEEE},
  title = {USMicroMagSet: {Using} {Deep} {Learning} {Analysis} to
    {Benchmark} the {Performance} of {Microrobots} in {Ultrasound}
    {Images}},
  journal = {IEEE Robotics and Automation Letters},
  volume = {8},
  number = {6},
  pages = {3254-3261},
  date = {2023-06},
  url = {https://dfolio.fr/publications/articles/2023botrosRAL.html},
  doi = {10.1109/LRA.2023.3264746},
  langid = {en-US},
  abstract = {Microscale robots introduce great perspectives into many
    medical applications such as drug delivery, minimally invasive
    surgery, and localized biometric diagnostics. Fully automatic
    microrobots’ real-time detection and tracking using medical imagers
    are actually investigated for future clinical translation.
    Ultrasound (US) B-mode imaging has been employed to monitor single
    agents and collective swarms of microrobots in vitro and ex vivo in
    controlled experimental conditions. However, low contrast and
    spatial resolution still limit the effective employment of such a
    method in a medical microrobotic scenario due to uncertainties
    associated with the position of microrobots. The positioning error
    arises due to the inaccuracy of the US-based visual feedback, which
    is provided by the detection and tracking algorithms. The
    application of deep learning networks is a promising solution to
    detect and track real-time microrobots in noisy ultrasonic images.
    However, what is most striking is the performance gap among
    state-of-the-art microrobots deep learning detection and tracking
    research. A key factor of that is the unavailability of large-scale
    datasets and benchmarks. In this paper, we present the first
    publicly available B-mode ultrasound dataset for microrobots
    (\$USmicroMagSet\$) with accurate annotations which contains more
    than 40000 samples of magnetic microrobots. In addition, for
    analyzing the performance of microrobots included in the proposed
    benchmark dataset, 4 deep learning detectors and 4 deep learning
    trackers are used.}
}