首页> 外文会议>International conference on medical image computing and computer-assisted intervention;MICCAI 2009 >Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts
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Multiple Sclerosis Lesion Segmentation Using an Automatic Multimodal Graph Cuts

机译:使用自动多模式图切的多发性硬化病变分割

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Graph Cuts have been shown as a powerful interactive segmentation technique in several medical domains. We propose to automate the Graph Cuts in order to automatically segment Multiple Sclerosis (MS) lesions in MRI. We replace the manual interaction with a robust EM-based approach in order to discriminate between MS lesions and the Normal Appearing Brain Tissues (NABT). Evaluation is performed in synthetic and real images showing good agreement between the automatic segmentation and the target segmentation. We compare our algorithm with the state of the art techniques and with several manual segmentations. An advantage of our algorithm over previously published ones is the possibility to semi-automatically improve the segmentation due to the Graph Cuts interactive feature.
机译:在许多医学领域中,Graph Cuts已被证明是一种强大的交互式分割技术。我们建议自动执行图切割,以便在MRI中自动分割多发性硬化(MS)病变。我们将基于健壮的基于EM的方法替换手动交互,以区分MS病变和正常出现的脑组织(NABT)。在合成图像和真实图像中进行评估,显示出自动分割和目标分割之间的良好一致性。我们将我们的算法与最新技术和几种手动分割方法进行了比较。我们的算法相对于以前发布的算法的一个优势是,由于Graph Cuts交互功能,可以半自动地改善分割效果。

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