Potesil Vaclav, Kadir Timor, Platsch Günther, Brady Sir Michael
Department of Engineering Science, University of Oxford.
Inf Process Med Imaging. 2011;22:333-45. doi: 10.1007/978-3-642-22092-0_28.
We propose a method for accurately localizing anatomical landmarks in 3D medical volumes based on dense matching of parts-based graphical models. Our novel approach replaces population mean models by jointly leveraging weighted combinations of labeled exemplars (both spatial and appearance) to obtain personalized models for the localization of arbitrary landmarks in upper body images. We compare the method to a baseline population-mean graphical model and atlas-based deformable registration optimized for CT-CT registration, by measuring the localization accuracy of 22 anatomical landmarks in clinical 3D CT volumes, using a database of 83 lung cancer patients. The average mean localization error across all landmarks is 2.35 voxels. Our proposed method outperforms deformable registration by 73%, 93% for the most improved landmark. Compared to the baseline population-mean graphical model, the average improvement of localization accuracy is 32%; 67% for the most improved landmark.
我们提出了一种基于基于部件的图形模型的密集匹配来精确地在三维医学体积中定位解剖标志点的方法。我们的新方法通过联合利用带标签示例（包括空间和外观）的加权组合来取代总体均值模型，从而获得用于上半身图像中任意标志点定位的个性化模型。我们通过测量83名肺癌患者临床三维CT体积中22个解剖标志点的定位准确性，将该方法与针对CT-CT配准优化的基线总体均值图形模型和基于图谱的可变形配准进行比较。所有标志点的平均定位误差为2.35体素。我们提出的方法在定位准确性方面比可变形配准提高了73%，对于改善最大的标志点提高了93%。与基线总体均值图形模型相比，定位准确性的平均提高为32%；对于改善最大的标志点提高了67%。
