集成自适应回归核的肿瘤生物靶区随机游走勾画方法

A random walk method with adaptive regression-kernel for delineation of biological target volumes of tumors

目的:评估一种肿瘤正电子发射断层扫描(PET)影像生物靶区的勾画新方法。方法:为有效区分PET影像中标准摄入值(Standard Uptake Value,SUV)相近的肿瘤区域和正常组织区域,首先计算肿瘤PET影像中每个体素点对应的自适应回归核,并将其集成到随机游走无向图的边权值函数中。然后利用三维自适应区域生长方法自动选取随机游走种子点,实现肿瘤PET生物靶区的自动勾画。结果:以临床放疗专家勾画的大体肿瘤区作为参考标准,本方法勾画的7例鼻咽癌病人PET生物靶区DICE相似性的均值为0.836 7,比仅基于PET SUV的随机游走勾画方法提高了4.31%,比基于PET SUV值和对比度纹理特征的随机游走勾画方法提高了3.34%。结论:集成自适应回归核的随机游走方法能够更精确地勾画肿瘤PET生物靶区。

Objective To access a novel method for the delineation of biological target volumes（BTV） of tumors in positron emission computed tomography（PET） images. Methods In order to effectively discriminate between normal tissues and tumors with similar standard uptake value（SUV） in PET images, the adaptive regression kernels of every voxel in PET images of tumors were calculated, and the adaptive regression kernels were integrated into the weight function of edges of undirected graph for random walk（RW）. Then the seeds of RW were automatically selected by three-dimensional adaptive region growing method to realize the automatic delineation of BTV in PET images. Results The PET images of 7 patients with nasopharyngeal carcinoma were used to evaluate the performances of the proposed method. The gross target volumes delineated manually by radiation oncologists were taken as a surrogate of the ground truth. The mean value of DICE similarities of BTV delineated by the proposed method in 7 cases of nasopharyngeal carcinoma was 0.837 6, which was increased by 4.31% as compared with RW only based on PET SUV and increased by 3.34% as compared with RW based on PET SUV and contrast. Conclusion The proposed RW algorithm with adaptive regression-kernel can delineate the BTV of tumors in PET images more accurately.