Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching.
Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching.
复制标题
基于肋骨解剖和结节模板匹配的连续 CT 扫描中的肺结节配准。
DOI：
10.1118/1.2712575
复制
发表时间：
2007
期刊：
影响因子：
3.8
通讯作者：
Wei,Jun
中科院分区：
文献类型：
--
作者：
Shi,Jiazheng;Sahiner,Berkman;Chan,Heang-Ping;Hadjiiski,Lubomir;Zhou,Chuan;Cascade,PhilipN;Bogot,Naama;Kazerooni,EllaA;Wu,Yi-Ta;Wei,Jun
正在开发一种自动化方法，以便在连续胸部CT扫描中识别相应的结节，用于间隔变化分析。该方法使用肋骨中心线作为初始结节配准的参考。空间自适应肋骨分割方法首先定位肋骨连接脊柱的区域，其定义肋骨跟踪的起始位置。通过期望最大化跟踪并局部分割每根肋骨。肋骨会自动标记，中心线会使用三角剖分进行估计。对于源扫描中的给定结节，识别最近的三个肋骨。由单纯形优化引导的三维（3D）刚性仿射变换对齐源和目标CT体积中的三个肋骨对中的每一个的中心线。沿着源扫描中的三根肋骨和目标扫描中的配准肋骨的中心线自动定义的控制点沿着用于引导使用第二3D刚性仿射变换的初始配准。然后在目标扫描中定位感兴趣的搜索体积（VOI）。搜索VOI内的噪声候选位置被识别为具有高Hessian响应的区域。通过搜索来自源扫描的结节模板与候选位置之间的最大互相关来细化初始配准。对20例患者的48次CT扫描进行了评价。有经验的放射科医生确定了101对相应的结节。三个指标用于性能评估。第一个度量是由放射科医师识别的结节中心与计算机配准之间的欧几里得距离，第二个度量是由放射科医师识别的结节VOI与计算机配准之间的体积重叠度量，并且第三个度量是命中率，其测量在目标扫描中由计算机配准计算的质心福尔斯由放射科医生平均欧氏距离误差为。只有两对的误差大于。平均体积重叠测量值为101对中有83对的比值大于0.5，只有2对没有重叠。最终命中率为。
An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation‐maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three‐dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross‐correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the radiologist. The average Euclidean distance error was . Only two pairs had an error larger than . The average volume overlap measure was Eighty‐three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was .