Minimizing joint risk of mislabeling for iterative Patch-based label fusion.
Minimizing joint risk of mislabeling for iterative Patch-based label fusion.
复制标题
最小化基于迭代斑块的标签融合的关节标签的关节风险。
DOI：
10.1007/978-3-642-40760-4_69
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Wu, Guorong;Wang, Qian;Liao, Shu;Zhang, Daoqiang;Nie, Feiping;Shen, Dinggang
医学图像中解剖结构的自动标记在许多神经科学研究中都是非常重要的。最近，基于非局部方式的基于块的标记被广泛地研究，以减少地图集与目标图像配准时可能出现的不对准。然而，在传统方法中，用于从注册的地图集进行标签融合的权重通常是独立计算的，因此缺乏防止模糊的地图集补丁对标签融合做出贡献的能力。更重要的是，这些权重通常只基于简单的斑块相似度来计算，因此不一定为标签融合提供最优解决方案。为了解决这些问题，我们提出了一种新的多图集场景下的基于块的标记融合方法，目标是用最具代表性的图集块来标记目标图像中的每个体素，同时也具有最低的联合误标记风险。具体地，使用稀疏编码来选择在目标图像的每个点处最好地表示底层面片的少量地图集补丁，从而最小化包括用于标记的误导性地图集补丁的机会。此外，我们通过分析任何一对地图集的形态错误模式的相关性以及地图集之间的标注共识，来检验任何一对地图集斑块在发生相似标注错误时的联合风险。该联合风险将根据最新的标签结果进一步递归更新，以纠正可能的标签错误。为了验证我们提出的方法的性能，我们在全脑分割和海马体分割上进行了评估，并与最新的方法进行了比较，获得了令人满意的标记结果。
Automated labeling of anatomical structures in medical images is very important in many neuroscience studies. Recently, patch-based labeling in the non-local manner has been widely investigated to alleviate the possible misalignment when registering atlases to the target image. However, the weights used for label fusion from the registered atlases in conventional methods are generally computed independently and thus lack the capability of preventing the ambiguous atlas patches from contributing to the label fusion. More critically, these weights are often calculated based only on the simple patch similarity, thus not necessarily providing optimal solution for label fusion. To address these issues, we present a novel patch-based label fusion method in multi-atlas scenario, for the goal of labeling each voxel in the target image by the best representative atlas patches that also have the lowest joint risk of mislabeling. Specifically, sparse coding is used to select a small number of atlas patches which best represent the underlying patch at each point of the target image, thus minimizing the chance of including the misleading atlas patches for labeling. Furthermore, we examine the joint risk of any pair of atlas patches in making similar labeling error, by analyzing the correlation of their morphological error patterns and also the labeling consensus among atlases. This joint risk will be further recursively updated based on the latest labeling results to correct the possible labeling errors. To demonstrate the performance of our proposed method, we have evaluated it on both whole brain parcellation and hippocampus segmentation, and achieved promising labeling results, compared with the state-of-the-art methods.
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影响因子：
10.6
作者：
Rousseau F;Habas PA;Studholme C
通讯作者：
Studholme C
影响因子：
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作者：
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DOI：
10.1111/j.2517-6161.1996.tb02080.x
发表时间：
1996-01-01
影响因子：
5.8
作者：
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通讯作者：
Tibshirani, R
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作者：
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作者：
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