Li D, Yang J, Yin Y
College of Physics and Electronics, Shandong Normal University, Ji nan, Shandong Province.
School of Information Science and Engineering, Shandong University, China, jinan.
Med Phys. 2012 Jun;39(6Part5):3645. doi: 10.1118/1.4734804.
In order to achieve tumor positioning for radiotherapy planning automatically and accurately, an efficient tumor positioning method is proposed by jointly registration and segmentation for 18F-FDG PET-CT scans.
At the first stage, the tumor is segmented from PET scans by region growing using the manual seeds which employs the SUV monotonous features, and then the tumor contours are transferred to corresponding CT images automatically for following radiation therapy planning by a new deformable registration method which is implemented by combining edge preserving scale space with the free form deformation. The edge preserving scale space which is able to select edges and contours of an image according to their geometric size is derived from the total variation model with the L1 norm (TV-L1). At each scale, the selected edges and contours are sufficiently strong to drive the deformation using the FFD grid, then the deformation fields are gained by a coarse to fine manner.Datasets were collected from 5 patients treated under the PET-CT scanner (GE medical systems, Discovery LS). Before treatment planning, the GTV (gross tumor volume) is delineated on every section of the PET scans by the radiation oncologist and the Result will be compared with proposed automatic segmentation method. Of the 5 patients investigated here, all are non-small cell lung carcinoma (NSCLC) patients.
After evaluation of the experiment results by three clinical oncologists, they concluded that the segmentation results are very close to the manual results and the GTV contours on CT scan which is produced by the deformation field automatically can be used for radiation therapy planning. The volumetric overlap is on an average 90%-97% comparing with manually segmented tumors by oncologists.
We can conclude that an efficient tumor positioning method is proposed by jointly registration and segmentation for FDG PET-CT datasets.
为了实现放射治疗计划中肿瘤的自动、精确定位，提出一种通过对18F-FDG PET-CT扫描进行联合配准和分割的高效肿瘤定位方法。
在第一阶段，利用采用SUV单调特征的手动种子点通过区域生长从PET扫描中分割肿瘤，然后通过一种新的可变形配准方法将肿瘤轮廓自动转移到相应的CT图像上，以便进行后续的放射治疗计划，该方法通过将保边尺度空间与自由形式变形相结合来实现。保边尺度空间能够根据图像的几何尺寸选择图像的边缘和轮廓，它是从具有L1范数的全变分模型（TV-L1）推导而来的。在每个尺度上，所选的边缘和轮廓足够强，以驱动使用FFD网格的变形，然后通过由粗到细的方式获得变形场。数据集来自在PET-CT扫描仪（GE医疗系统，Discovery LS）下接受治疗的5名患者。在治疗计划之前，放射肿瘤学家在PET扫描的每个切片上勾勒出GTV（大体肿瘤体积），并将结果与所提出的自动分割方法进行比较。在这里研究的5名患者中，均为非小细胞肺癌（NSCLC）患者。
在由三名临床肿瘤学家对实验结果进行评估后，他们得出结论，分割结果与手动结果非常接近，并且通过变形场自动生成的CT扫描上的GTV轮廓可用于放射治疗计划。与肿瘤学家手动分割的肿瘤相比，体积重叠平均为90%-97%。
我们可以得出结论，通过对FDG PET-CT数据集进行联合配准和分割，提出了一种高效的肿瘤定位方法。
