Corso Jason J, Sharon Eitan, Yuille Alan
Medical Imaging Informatics, University of California, Los Angeles, CA, USA.
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):790-8. doi: 10.1007/11866763_97.
We present a new method for automatic segmentation of heterogeneous image data, which is very common in medical image analysis. The main contribution of the paper is a mathematical formulation for incorporating soft model assignments into the calculation of affinities, which are traditionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm. We apply the technique to the task of detecting and segmenting brain tumor and edema in multimodal MR volumes. Our results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of brain tumor.
我们提出了一种用于自动分割异质图像数据的新方法，这种数据在医学图像分析中非常常见。本文的主要贡献是一种数学公式，用于将软模型分配纳入亲和力计算中，而传统上亲和力计算是无模型的。我们通过加权聚合算法将得到的模型感知亲和力集成到多级分割中。我们将该技术应用于在多模态磁共振体积中检测和分割脑肿瘤及水肿的任务。我们的结果表明，在脑肿瘤这种困难情况下，将模型感知亲和力纳入分割过程是有益的。
