首页> 外文会议>6th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2003) Pt.II; Nov 15-18, 2003; Montreal, Canada >Nonlinear Diffusion Scale-Space and Fast Marching Level Sets for Segmentation of MR Imagery and Volume Estimation of Stroke Lesions
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Nonlinear Diffusion Scale-Space and Fast Marching Level Sets for Segmentation of MR Imagery and Volume Estimation of Stroke Lesions

机译:MR图像分割和中风病灶体积估计的非线性扩散尺度空间和快速行进水平集

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摘要

We combine nonlinear diffusion scale-space and geometric deformable models for segmenting lesions in MR images of ischemic stroke patients. Region and boundary information are integrated in a speed function for robust segmentation with the fast marching level set method. A confidence-based model of segmentation captures the significant variability in human segmentation and the ambiguity inherent in many lesions, and it provides a testbed for a new measure of variance with sets as random variables. This method offers users a family of segmentations, requires less user input than previous methods, and its volume estimates effectively match those of doctors' hand segmentations.
机译:我们结合非线性扩散尺度空间和几何变形模型来分割缺血性中风患者的MR图像中的病变。区域和边界信息集成在速度函数中,可使用快速行进级别设置方法进行鲁棒分割。基于置信度的分割模型捕获了人类分割中的显着可变性以及许多病变固有的歧义性,并且它为以集合为随机变量的新的方差度量提供了试验平台。与以前的方法相比,此方法为用户提供了一系列细分,所需的用户输入更少,并且其数量估算有效地与医生的手部细分相匹配。

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