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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Multi-Atlas Segmentation with Joint Label Fusion
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Multi-Atlas Segmentation with Joint Label Fusion

机译：联合标签融合的多图集分割

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摘要

Multi-atlas segmentation is an effective approach for automatically labeling objects of interest in biomedical images. In this approach, multiple expert-segmented example images, called atlases, are registered to a target image, and deformed atlas segmentations are combined using label fusion. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity have been particularly successful. However, one limitation of these strategies is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this limitation, we propose a new solution for the label fusion problem in which weighted voting is formulated in terms of minimizing the total expectation of labeling error and in which pairwise dependency between atlases is explicitly modeled as the joint probability of two atlases making a segmentation error at a voxel. This probability is approximated using intensity similarity between a pair of atlases and the target image in the neighborhood of each voxel. We validate our method in two medical image segmentation problems: hippocampus segmentation and hippocampus subfield segmentation in magnetic resonance (MR) images. For both problems, we show consistent and significant improvement over label fusion strategies that assign atlas weights independently.
机译：多图谱分割是一种自动标记生物医学图像中感兴趣对象的有效方法。在这种方法中，将多个称为图集的专家分段示例图像配准到目标图像，然后使用标签融合来合并变形的图集分割。在提议的标签融合策略中，具有从地图集-目标强度相似性得出的空间变化的权重分布的加权投票已特别成功。但是，这些策略的局限性在于，每个图集的权重是独立计算的，而没有考虑不同图集可能产生相似标签错误的事实。为了解决这个限制，我们为标签融合问题提出了一种新的解决方案，其中加权投票的制定是为了最大程度地减少标签错误的总期望，并且图集之间的成对依赖性明确地建模为两个图集的联合概率。体素上的分割错误。使用一对地图集和每个体素附近的目标图像之间的强度相似度来近似此概率。我们在两个医学图像分割问题中验证了我们的方法：磁共振（MR）图像中的海马分割和海马子场分割。对于这两个问题，我们显示了与独立分配地图集权重的标签融合策略一致且显着的改进。

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