Core Concepts
Enhancing needle detection in ultrasound images using VibNet framework.
Abstract
Introduction to the challenges of needle visibility in ultrasound-guided procedures.
Existing methods to improve needle visibility, such as beam steering and Doppler imaging.
Proposal of VibNet framework utilizing vibration-induced motion for robust needle detection.
Detailed explanation of VibNet's modules: motion extraction, frequency feature aggregation, and needle localization.
Experimental setup, dataset details, and evaluation metrics for performance comparison.
Results showing VibNet outperforming other frameworks in terms of accuracy and outlier reduction.
Generalizability assessment on different ex vivo animal tissues and ablation study results.
Conclusion highlighting the effectiveness of VibNet for precise needle detection in ultrasound images.
Stats
Based on the results obtained on distinct ex vivo porcine and bovine tissue samples, the proposed algorithm exhibits superior detection performance with efficient computation and generalization capability.
Quotes
"In clinical applications that involve ultrasound-guided intervention, the visibility of the needle can be severely impeded due to steep insertion and strong distractors."
"To address this challenge, we propose VibNet, a learning-based framework tailored to enhance the robustness and accuracy of needle detection in ultrasound images."