Medical Instrument Detection in Ultrasound-Guided Interventions: A Review

TL;DR

This review paper discusses various methods for detecting medical instruments in ultrasound-guided interventions, highlighting traditional and data-driven approaches, clinical applications, and future research directions.

Contribution

It provides a comprehensive overview of instrument detection techniques in ultrasound, comparing non-data-driven and data-driven methods and identifying key challenges and research opportunities.

Findings

Data-driven methods are increasingly used for instrument detection.

Clinical applications include anesthesia, biopsy, prostate brachytherapy, and cardiac catheterization.

The paper highlights future research directions in the field.

Abstract

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.