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Kimbowa et al. Art Int Surg 2024;4:149-69 Artificial
DOI: 10.20517/ais.2024.20
Intelligence Surgery

Review Open Access

Advancements in needle visualization enhancement
and localization methods in ultrasound: a literature

review

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Alvin Kimbowa 1 , Alex Pieters , Parsa Tadayon , Ishi Arora , Sabrina Gulam , Antonio Pinos , David
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Liu , Ilker Hacihaliloglu 1,2,3
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School of Biomedical Engineering, The University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.
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Department of Radiology, The University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.
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Department of Medicine, The University of British Columbia, Vancouver V6T 1Z4, British Columbia, Canada.
Correspondence to: Alvin Kimbowa, School of Biomedical Engineering, The University of British Columbia, Center for Aging
Smart 6/F 2635 Laurel Street, Vancouver V6T 1Z4, British Columbia, Canada. E-mail: alvinbk@student.ubc.ca
How to cite this article: Kimbowa A, Pieters A, Tadayon P, Arora I, Gulam S, Pinos A, Liu D, Hacihaliloglu I. Advancements in
needle visualization enhancement and localization methods in ultrasound: a literature review. Art Int Surg 2024;4:149-69. https:/
/dx.doi.org/10.20517/ais.2024.20
Received: 15 Mar 2024 First Decision: 31 May 2024 Revised: 18 Jul 2024 Accepted: 22 Jul 2024 Published: 31 Jul 2024

Academic Editor: Andrew A. Gumbs Copy Editor: Dong-Li Li Production Editor: Dong-Li Li

Abstract
Ultrasound guidance plays a central role in numerous minimally invasive procedures involving percutaneous needle
insertion, ensuring safe and accurate needle placement. However, it encounters two primary challenges: (1)
aligning the needle with the ultrasound beam and (2) visualizing the needle even when correctly aligned. In this
review, we offer a concise overview of the physics foundation underlying these challenges and explore various
approaches addressing specific challenges, with a focus on software-based solutions. We further distinguish
between hardware-based and software-based solutions, placing a stronger emphasis on the latter. The
incorporation of artificial intelligence into these methods to enhance needle visualization and localization is briefly
discussed. We identify state-of-the-art needle detection methods, showcasing submillimeter precision in tip
localization and orientation. Additionally, we provide insights into potential future directions, aiming to facilitate
the translation of these advanced methods into the clinic. This article serves as a comprehensive guide, offering
insights into challenges, evolving solutions, and prospective research directions to effectively address these issues.

Keywords: Needle enhancement, segmentation, localization, ultrasound, biopsy, machine learning, deep learning

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made.

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