March 22, 2024, 4:46 a.m. | Chenyang Li, Dianye Huang, Angelos Karlas, Nassir Navab, Zhongliang Jiang

cs.CV updates on arXiv.org arxiv.org

arXiv:2403.14523v1 Announce Type: cross
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 …

abstract accuracy applications arxiv challenge clinical cs.cv detection eess.iv framework noise robustness type visibility

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