School of Mechanical Engineering, Tianjin University, Tianjin, China.
J Appl Clin Med Phys. 2021 Apr;22(4):121-131. doi: 10.1002/acm2.13231. Epub 2021 Mar 25.
To develop a method for automatically detecting needles from CT images, which can be used in image-guided lung interstitial brachytherapy to assist needle placement assessment and dose distribution optimization.
Based on the preview model parameters evaluation, local optimization combining local random sample consensus, and principal component analysis, the needle shaft was detected quickly, accurately, and robustly through the modified random sample consensus algorithm. By tracing intensities along the axis, the needle tip was determined. Furthermore, multineedles in a single slice were segmented at once using successive inliers deletion.
The simulation data show that the segmentation efficiency is much higher than the original random sample consensus and yet maintains a stable submillimeter accuracy. Experiments with physical phantom demonstrate that the segmentation accuracy of described algorithm depends on the needle insertion depth into the CT image. Application to permanent lung brachytherapy image is also validated, where manual segmentation is the counterparts of the estimated needle shape.
From the results, the mean errors in determining needle orientation and endpoint are regulated within 2° and 1 mm, respectively. The average segmentation time is 0.238 s per needle.
开发一种从 CT 图像中自动检测针的方法，可用于图像引导下的肺间质近距离放疗，以辅助针的放置评估和剂量分布优化。
基于预览模型参数评估，采用局部优化结合局部随机抽样一致和主成分分析，通过改进的随机抽样一致算法快速、准确、稳健地检测针杆。通过沿轴线跟踪强度，确定针尖。此外，使用连续内点删除可以一次性对单个切片中的多根针进行分割。
模拟数据表明，与原始随机抽样一致相比，分割效率大大提高，但仍保持稳定的亚毫米精度。使用物理体模的实验表明，所描述算法的分割精度取决于针插入 CT 图像的深度。该方法还应用于永久性肺近距离放疗图像，其中手动分割是估计针形状的对照。
从结果来看，确定针方向和端点的平均误差分别控制在 2°和 1mm 以内。平均分割时间为每根针 0.238s。
