Invisible Needle Detection in Ultrasound: Leveraging Mechanism-Induced
  Vibration
Chenyang Li Dianye Huang Angelos Karlas Nassir Navab Zhongliang Jiang
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.