• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >The evaluation of a highly automated mixture model based technique for PET tumor volume segmentation
【24h】

The evaluation of a highly automated mixture model based technique for PET tumor volume segmentation

机译：基于高度混合模型的PET肿瘤体积分割技术的评估

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PET-based tumor volume segmentation techniques are under investigation in recent years due to the increased utilization of FDG-PET imaging in radiation therapy. We have taken the approach of using a Gaussian mixture model (GMM) to model the image intensity distribution of a selected 3D region that completely covers the tumor, called the "analysis region". The modeling is performed with a predetermined number of Gaussian classes and results in a classification of every voxel into one of these classes. The classes are then grouped together to obtain the tumor volume. The only user interaction required is the selection of the "analysis region" and then the algorithm proceeds automatically to initialize the parameters of the different classes and finds the maximum likelihood estimate with expectation maximization. We used 13 clinical and 19 phantom cases to evaluate the precision and accuracy of the segmentation. Reproducibility was within 10% of the average tumor volume estimate and accuracy was ±35% of the true tumor volume and better when compared to two other proposed techniques. The GMM segmentation is extremely user friendly with good precision and accuracy. It has shown great potential to be used in the clinical environment.
机译：近年来，由于FDG-PET影像在放射治疗中的越来越多的利用，正在研究基于PET的肿瘤体积分割技术。我们采用了一种使用高斯混合模型（GMM）来建模选定3D区域的图像强度分布的方法，该区域完全覆盖了肿瘤，称为“分析区域”。使用预定数量的高斯类别执行建模，并导致将每个体素分类为这些类别之一。然后将这些类别分组在一起以获得肿瘤体积。唯一需要的用户交互是“分析区域”的选择，然后算法自动进行以初始化不同类别的参数，并找到具有期望最大化的最大似然估计。我们使用了13例临床病例和19例幻像病例来评估分割的准确性和准确性。可重现性在平均肿瘤体积估计值的10％以内，准确度为真实肿瘤体积的±35％，与其他两种拟议技术相比，更好。 GMM细分非常人性化，具有良好的精度和准确性。它显示出在临床环境中使用的巨大潜力。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号