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.

这篇论文提出了一种新的三维小物体医疗标记检测方法，通过识别小型单环的中心作为针穿入点，基于峰值变换增强技术为数据集进行增强，提取特征图的 ROI 生成掩模图，并通过 ROI 点云轮廓拟合的方式获取深度图中 ROI 的点云，最后通过 Tukey 损失优化方法实现高精度和高稳定性定位以及任意针穿入点的定位。

傅里叶增强的多模式 3D 小物体光学标记识别和穿刺手术导航定位方法

A Fourier-enhanced multi-modal 3D small object optical mark recognition  and positioning method for percutaneous abdominal puncture surgical  navigation