Invisible Needle Detection in Ultrasound: Leveraging Mechanism-Induced Vibration

• URL: http://arxiv.org/abs/2403.14523v1
• Date: Thu, 21 Mar 2024 16:23:25 GMT
• Title: Invisible Needle Detection in Ultrasound: Leveraging Mechanism-Induced Vibration
• Authors: Chenyang Li, Dianye Huang, Angelos Karlas, Nassir Navab, Zhongliang Jiang,
• Abstract summary: VibNet is a learning-based framework tailored to enhance the robustness of needle detection in ultrasound images. Inspired by Eulerian Video Magnification techniques, we utilize an external step motor to induce low-amplitude periodic motion on the needle. To robustly and precisely detect the needle leveraging these vibrations, VibNet integrates the learning-based Short-Time-ier-Transform and Hough-Transform modules.
• Score: 41.242444481240135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In clinical applications that involve ultrasound-guided intervention, the visibility of the needle can be severely impeded due to steep insertion and strong distractors such as speckle noise and anatomical occlusion. To address this challenge, we propose VibNet, a learning-based framework tailored to enhance the robustness and accuracy of needle detection in ultrasound images, even when the target becomes invisible to the naked eye. Inspired by Eulerian Video Magnification techniques, we utilize an external step motor to induce low-amplitude periodic motion on the needle. These subtle vibrations offer the potential to generate robust frequency features for detecting the motion patterns around the needle. To robustly and precisely detect the needle leveraging these vibrations, VibNet integrates learning-based Short-Time-Fourier-Transform and Hough-Transform modules to achieve successive sub-goals, including motion feature extraction in the spatiotemporal space, frequency feature aggregation, and needle detection in the Hough space. 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.

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