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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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TipDet: A multi-keyframe motion-aware framework for tip detection during ultrasound-guided interventions.

Ruixin Wang¹, Guoping Tan¹, Xiaohui Liu²

¹College of Computer and Information, Hohai University, Nanjing 210098, China.

Computer Methods and Programs in Biomedicine

|March 9, 2024

Summary

A new framework, TipDet, precisely detects needle tips in ultrasound images by analyzing motion patterns. This advancement improves accuracy and facilitates safer ultrasound-guided procedures.

Keywords:

Deep learning Motion pattern learning Needle tip detection Optical flow Ultrasound-guided interventions

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Area of Science:

Medical Imaging
Computer Vision
Interventional Radiology

Background:

Automatic needle tip detection is crucial for ultrasound (US)-guided interventional procedures.
Challenges include spatial indiscernibility, background interference, and indistinct tip appearance in US images.

Purpose of the Study:

To propose a novel multi-keyframe motion-aware framework, TipDet, for precise needle tip detection in US images.
To overcome spatial indiscernibility and improve localization accuracy.

Main Methods:

TipDet utilizes short-term spatial-temporal and long-term motion patterns for tip identification.
An adaptive keyframe model (AKM) selects informative frames for motion learning.
A two-stream backbone (TSB) detects candidate tips, refined by optical-flow-aware multi-head cross-attention (OFA-MHCA) for long-term motion patterns.

Main Results:

TipDet achieved 78.7% AP0.1:0.5 on a clinical dataset of 4195 B-mode images.
Demonstrated an 8.9% improvement over the base detector at approximately 20 FPS.
Achieved a tip localization error of 1.3±0.6%, outperforming existing methods.

Conclusions:

TipDet provides robust and precise needle tip localization for US-guided interventions.
The framework enhances the applicability and ease of US-guided procedures.
Open-source code and data are available for further research and development.