基于RANSAC的新方法,用于检测和估计外部附着标记球在头和面部CT图像中
Yonghui Li1, Han Zhang2, Weili Shi1
1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, China.
Quantitative imaging in medicine and surgery
|September 2, 2025
概括
这项研究引入了一种可靠的方法,用于精确的球形参数估计. 通过使用LEO-RANSAC算法提高了精度,提高了手术导航的准确性和可靠性.
科学领域:
- 医学成像
- 计算机辅助的手术
- 外科导航
背景情况:
- 从CT图像中精确估计球形参数对于头和面部外科导航中的图像物理空间注册至关重要.
- 现有的方法容易受到人工物和干扰的影响,影响精度.
- 需要强大的方法来满足高精度的外科应用要求.
研究的目的:
- 从CT图像中开发一个可靠的球形参数估计方法.
- 提高手术导航系统的可复制性和准确性,尤其是噪音数据.
- 为了提高头和面部外科导航的可靠性.
主要方法:
- 提出了一个局部评估和优化RANdom SAmple共识 (LEO-RANSAC) 算法来改进球形参数检测.
- 引入了一种结合多级自适应曲率和局部解决方案的新型度量.
- 使用定制设备进行信托定位错误 (FLE) 测量,以及用于信托注册错误 (FRE) 和目标注册错误 (TRE) 评估的头骨幻影研究.
主要成果:
- 在72点云上进行评估,其内向比率从30%到90%.
- 87. 50%的最大FLE小于0. 9毫米,95. 83%的FLE差异小于0. 01
- 头骨幻影研究结果为0.4222,0.5223,0.372毫米,TREs为0.8546,0.9471,0.8537毫米.
结论:
- 拟议的LEO-RANSAC方法与现有方法相比,具有更高的准确性和可靠性.
- 这种方法有效地处理低内侧比率数据,提高了手术导航的稳定性.
- 结果突出了该方法在高精度口面外科导航应用中的潜力.
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