Kuo Nathanael, Lee Junghoon, Deguet Anton, Song Danny, Burdette E Clif, Prince Jerry
Dept. of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Proc SPIE Int Soc Opt Eng. 2010 Feb 23;7625. doi: 10.1117/12.844520.
C-arm X-ray fluoroscopy-based radioactive seed localization for intraoperative dosimetry of prostate brachytherapy is an active area of research. The fluoroscopy tracking (FTRAC) fiducial is an image-based tracking device composed of radio-opaque BBs, lines, and ellipses that provides an effective means for pose estimation so that three-dimensional reconstruction of the implanted seeds from multiple X-ray images can be related to the ultrasound-computed prostate volume. Both the FTRAC features and the brachytherapy seeds must be segmented quickly and accurately during the surgery, but current segmentation algorithms are inhibitory in the operating room (OR). The first reason is that current algorithms require operators to manually select a region of interest (ROI), preventing automatic pipelining from image acquisition to seed reconstruction. Secondly, these algorithms fail often, requiring operators to manually correct the errors. We propose a fast and effective ROI-free automatic FTRAC and seed segmentation algorithm to minimize such human intervention. The proposed algorithm exploits recent image processing tools to make seed reconstruction as easy and convenient as possible. Preliminary results on 162 patient images show this algorithm to be fast, effective, and accurate for all features to be segmented. With near perfect success rates and subpixel differences to manual segmentation, our automatic FTRAC and seed segmentation algorithm shows promising results to save crucial time in the OR while reducing errors.
基于C形臂X射线荧光透视的放射性粒子定位用于前列腺近距离治疗术中剂量测定是一个活跃的研究领域。荧光透视跟踪（FTRAC）基准物是一种基于图像的跟踪设备，由不透射线的BB弹、线条和椭圆组成，它为姿态估计提供了一种有效手段，以便从多个X射线图像对植入粒子进行三维重建，并与超声计算的前列腺体积相关联。在手术过程中，FTRAC特征和近距离治疗粒子都必须快速且准确地分割，但当前的分割算法在手术室（OR）中存在阻碍。第一个原因是当前算法要求操作人员手动选择感兴趣区域（ROI），这妨碍了从图像采集到粒子重建的自动流水线操作。其次，这些算法经常失败，需要操作人员手动纠正错误。我们提出一种快速有效的无ROI自动FTRAC和粒子分割算法，以尽量减少此类人工干预。所提出的算法利用了最新的图像处理工具，使粒子重建尽可能简单方便。对162例患者图像的初步结果表明，该算法对于所有要分割的特征来说快速、有效且准确。我们的自动FTRAC和粒子分割算法成功率近乎完美，与手动分割的亚像素差异很小，显示出在手术室节省关键时间并减少错误方面的良好前景。
