Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Med Phys. 2011 Oct;38(10):5351-61. doi: 10.1118/1.3633902.
A feature based deformable registration model with sliding transformation was developed in the upper abdominal region for liver cancer.
A two-step thin-plate spline (bi-TPS) algorithm was implemented to deformably register the liver organ. The first TPS registration was performed to exclusively quantify the sliding displacement component. A manual segmentation of the thoracic and abdominal cavity was performed as a priori knowledge. Tissue feature points were automatically identified inside the segmented contour on the images. The scale invariant feature transform method was utilized to match feature points that served as landmarks for the subsequent TPS registration to derive the sliding displacement vector field. To a good approximation, only motion along superior/inferior (SI) direction of voxels on each slice was averaged to obtain the sliding displacement for each slice. A second TPS transformation, as the last step, was carried out to obtain the local deformation field. Manual identification of bifurcation on liver, together with the manual segmentation of liver organ, was employed as a "ground truth" for assessing the algorithm's performance.
The proposed two-step TPS was assessed with six liver patients. The average error of liver bifurcation between manual identification and calculation for these patients was less than 1.8 mm. The residual errors between manual contour and propagated contour of liver organ using the algorithm fell in the range between 2.1 and 2.8 mm. An index of Dice similarity coefficient (DSC) between manual contour and calculated contour for liver tumor was 93.6% compared with 71.2% from the conventional TPS calculation.
A high accuracy (∼2 mm) of the two-step feature based TPS registration algorithm was achievable for registering the liver organ. The discontinuous motion in the upper abdominal region was properly taken into consideration. Clinical implementation of the algorithm will find broad application in radiation therapy of liver cancer.
为肝癌开发了一种基于特征的可变形配准模型，具有滑动变换，用于上腹部区域。
实施两步薄板样条（bi-TPS）算法对肝脏器官进行可变形配准。首先进行 TPS 配准以专门量化滑动位移分量。手动分割胸腹腔作为先验知识。在图像上的分割轮廓内自动识别组织特征点。利用尺度不变特征变换方法匹配特征点，这些特征点作为后续 TPS 配准的地标，以得出滑动位移矢量场。作为近似，仅沿每个切片的 superior/inferior (SI) 方向对体素的运动进行平均，以获得每个切片的滑动位移。作为最后一步，进行第二次 TPS 变换以获得局部变形场。肝脏分支的手动识别，以及肝脏器官的手动分割，用作评估算法性能的“真实情况”。
对六名肝脏患者评估了提出的两步 TPS。这些患者的肝脏分支手动识别和计算之间的平均误差小于 1.8 毫米。使用该算法的肝脏器官手动轮廓和传播轮廓之间的残余误差在 2.1 到 2.8 毫米之间。与传统 TPS 计算相比，肝脏肿瘤的手动轮廓和计算轮廓之间的 Dice 相似性系数（DSC）指数为 93.6%，而 DSC 指数为 71.2%。
两步基于特征的 TPS 配准算法能够实现高精度（约 2 毫米）的肝脏器官配准。考虑到上腹部区域的不连续运动。该算法的临床实施将在肝癌放射治疗中得到广泛应用。
