  Medical instrument detection is essential for computer-assisted interventions
since it would facilitate the surgeons to find the instrument efficiently with
a better interpretation, which leads to a better outcome. This article reviews
medical instrument detection methods in the ultrasound-guided intervention.
First, we present a comprehensive review of instrument detection methodologies,
which include traditional non-data-driven methods and data-driven methods. The
non-data-driven methods were extensively studied prior to the era of machine
learning, i.e. data-driven approaches. We discuss the main clinical
applications of medical instrument detection in ultrasound, including
anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization, which
were validated on clinical datasets. Finally, we selected several principal
publications to summarize the key issues and potential research directions for
the computer-assisted intervention community.
