基于回归的标签融合用于多图谱分割
Regression-Based Label Fusion for Multi-Atlas Segmentation.
作者信息
Wang Hongzhi, Suh Jung Wook, Das Sandhitsu, Pluta John, Altinay Murat, Yushkevich Paul
出版信息
Conf Comput Vis Pattern Recognit Workshops. 2011 Jun 20:1113-1120. doi: 10.1109/CVPR.2011.5995382.
Abstract
Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques.
摘要
使用多图谱标签融合的自动分割已在医学图像分析中得到广泛应用。为了简化标签融合问题，大多数方法隐含地做出了一个强有力的假设，即不同图谱产生的分割误差是不相关的。我们表明，违背这一假设会显著降低多图谱分割的效率。为了解决这个问题，我们提出了一种基于回归的标签融合方法。我们在磁共振成像（MRI）中分割海马体的实验表明，相较于之前的标签融合技术有显著改进。
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