A transversal approach for patch-based label fusion via matrix completion.
A transversal approach for patch-based label fusion via matrix completion.
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DOI：
10.1016/j.media.2015.06.002
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发表时间：
2015-08
影响因子：
10.9
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Sanroma G;Wu G;Gao Y;Thung KH;Guo Y;Shen D
近年来，基于多图谱块的标签融合在医学图像分割领域受到越来越多的关注。在通过配准将解剖标签从图谱图像扭曲到目标图像之后，标签融合是确定每个目标图像点的潜在标签的关键步骤。两种流行类型的基于块的标签融合方法是（1）基于重建的方法，其将目标标签计算为图谱标签的加权平均，其中通过使用图谱图像块重建目标图像块来导出权重;以及（2）将目标标签确定为目标图像块的映射的基于分类的方法，其中映射函数通常使用图谱图像块及其对应的标签来学习。这两种方法都有其优点和局限性。在本文中，我们提出了一种新的补丁为基础的标签融合方法，联合收割机结合上述两种类型的方法通过矩阵完成（因此，我们称之为横向）。正如我们将展示的那样，我们的方法克服了基于重建和基于分类的方法的各自局限性。由于标记置信度可能会在目标图像点之间变化，我们进一步提出了一个顺序标记框架，该框架首先标记高置信度点，然后在先前迭代中确定的标签信息的指导下，以迭代方式逐渐标记更具挑战性的点。我们展示了我们的新标签融合方法在分割ADNI数据集中的海马，LONI数据集中的皮质下和边缘结构以及SATA数据集中的中脑结构方面的性能。我们实现了更准确的分割结果比基于重建和基于分类的方法。我们的标签融合方法也在在线SATA多图谱分割挑战赛中排名第一。
Recently, multi-atlas patch-based label fusion has received an increasing interest in the medical image segmentation field. After warping the anatomical labels from the atlas images to the target image by registration, label fusion is the key step to determine the latent label for each target image point. Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing the target image patch using the atlas image patches; and (2) classification-based approaches that determine the target label as a mapping of the target image patch, where the mapping function is often learned using the atlas image patches and their corresponding labels. Both approaches have their advantages and limitations. In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal). As we will show, our method overcomes the individual limitations of both reconstruction-based and classification-based approaches. Since the labeling confidences may vary across the target image points, we further propose a sequential labeling framework that first labels the highly confident points and then gradually labels more challenging points in an iterative manner, guided by the label information determined in the previous iterations. We demonstrate the performance of our novel label fusion method in segmenting the hippocampus in the ADNI dataset, subcortical and limbic structures in the LONI dataset, and mid-brain structures in the SATA dataset. We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge.