  Navigation for thoracoabdominal puncture surgery is used to locate the needle
entry point on the patient's body surface. The traditional reflective ball
navigation method is difficult to position the needle entry point on the soft,
irregular, smooth chest and abdomen. Due to the lack of clear characteristic
points on the body surface using structured light technology, it is difficult
to identify and locate arbitrary needle insertion points. Based on the high
stability and high accuracy requirements of surgical navigation, this paper
proposed a novel method, a muti-modal 3D small object medical marker detection
method, which identifies the center of a small single ring as the needle
insertion point. Moreover, this novel method leverages Fourier transform
enhancement technology to augment the dataset, enrich image details, and
enhance the network's capability. The method extracts the Region of Interest
(ROI) of the feature image from both enhanced and original images, followed by
generating a mask map. Subsequently, the point cloud of the ROI from the depth
map is obtained through the registration of ROI point cloud contour fitting. In
addition, this method employs Tukey loss for optimal precision. The
experimental results show this novel method proposed in this paper not only
achieves high-precision and high-stability positioning, but also enables the
positioning of any needle insertion point.
