Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC 20010, USA.
IEEE Trans Med Imaging. 2012 Oct;31(10):1965-76. doi: 10.1109/TMI.2012.2211887. Epub 2012 Aug 7.
The paper presents the automated computation of hepatic tumor burden from abdominal computed tomography (CT) images of diseased populations with images with inconsistent enhancement. The automated segmentation of livers is addressed first. A novel 3-D affine invariant shape parameterization is employed to compare local shape across organs. By generating a regular sampling of the organ's surface, this parameterization can be effectively used to compare features of a set of closed 3-D surfaces point-to-point, while avoiding common problems with the parameterization of concave surfaces. From an initial segmentation of the livers, the areas of atypical local shape are determined using training sets. A geodesic active contour corrects locally the segmentations of the livers in abnormal images. Graph cuts segment the hepatic tumors using shape and enhancement constraints. Liver segmentation errors are reduced significantly and all tumors are detected. Finally, support vector machines and feature selection are employed to reduce the number of false tumor detections. The tumor detection true position fraction of 100% is achieved at 2.3 false positives/case and the tumor burden is estimated with 0.9% error. Results from the test data demonstrate the method's robustness to analyze livers from difficult clinical cases to allow the temporal monitoring of patients with hepatic cancer.
本文提出了一种从患有疾病的人群的腹部计算机断层扫描（CT）图像中自动计算肝肿瘤负担的方法，这些图像的增强效果不一致。首先解决肝脏的自动分割问题。采用新颖的 3-D 仿射不变形状参数化方法来比较器官之间的局部形状。通过对器官表面进行规则采样，这种参数化方法可以有效地用于对点到点的一组闭合 3-D 曲面的特征进行比较，同时避免了参数化凹面的常见问题。从肝脏的初始分割开始，使用训练集确定异常图像中局部形状的不规则区域。测地线活动轮廓局部修正肝脏的分割。使用形状和增强约束进行图形切割以分割肝肿瘤。显著减少了肝脏分割错误，并检测到了所有肿瘤。最后，使用支持向量机和特征选择来减少假肿瘤检测的数量。在 2.3 个假阳性/例的情况下，实现了肿瘤检测真阳性率为 100%，肿瘤负担的估计误差为 0.9%。测试数据的结果表明，该方法具有从困难的临床病例中分析肝脏的鲁棒性，以允许对肝癌患者进行时间监测。
