[1]刘国才,官文静,田娟秀,等. 集成自适应回归核的肿瘤生物靶区随机游走勾画方法[J].中国医学物理学杂志,2018,35(7):758-765.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
　LIU Guocai,GUAN Wenjing,TIAN Juanxiu,et al. A random walk method with adaptive regression-kernel for delineation of biological target volumes of tumors[J].Chinese Journal of Medical Physics,2018,35(7):758-765.[doi:DOI:10.3969/j.issn.1005-202X.2018.07.004]
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 集成自适应回归核的肿瘤生物靶区随机游走勾画方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
35卷
期数:
2018年第7期
页码:
758-765
栏目:
医学放射物理
出版日期:
2018-07-20

文章信息/Info

Title:
 A random walk method with adaptive regression-kernel for delineation of biological target volumes of tumors
文章编号:
1005-202X（2018）07-0758-08
作者:
 刘国才1; 官文静1; 田娟秀1; 朱苏雨2; 鞠忠建3
 1.湖南大学电气与信息工程学院， 湖南 长沙 410082； 2.中南大学湘雅医学院附属湖南省肿瘤医院放疗科， 湖南 长沙 410013；
3.中国人民解放军总医院放疗科， 北京 100853
Author(s):
 LIU Guocai1;  GUAN Wenjing1;  TIAN Juanxiu1;  ZHU Suyu2;  JU Zhongjian3
 1. College of Electrical and Information Engineering, Hunan University, Changsha 410082, China; 2. Department of Radiotherapy, Hunan Cancer Hospital, Xiangya School of Medicine, Central South University, Changsha 410013, China; 3. Department of Radiotherapy, Chinese PLA General Hospital, Beijing 100853, China
关键词:
 ; 医学图像分割; 自适应核回归; 随机游走; 肿瘤靶区勾画; 调强放疗; 鼻咽癌
Keywords:
 ; Keywords: medical image segmentation;  adaptive kernel regression;  random walk;  target volume delineation;  intensity-modulated radiotherapy;  nasopharyngeal carcinoma
分类号:
R312；TP391.4
DOI:
DOI:10.3969/j.issn.1005-202X.2018.07.004
文献标志码:
A
摘要:
 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。
Abstract:
 Abstract: 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.

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备注/Memo

备注/Memo:
 【收稿日期】2017-11-23
【基金项目】湖南省科技计划项目（2016WK2001）；国家自然科学基金（61671204, 61471166, 61771189）
【作者简介】刘国才，博士，教授，主要从事医学图像处理与智能医疗系统研究，E-mail: lgc630819@hnu.edu.cn
更新日期/Last Update: 2018-07-24