Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution.
Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution.
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DOI：
10.1007/978-3-319-24574-4_23
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发表时间：
2015-10
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
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作者：
Song Y;Wu G;Sun Q;Bahrami K;Li C;Shen D
医学图像中解剖结构的准确分割在神经科学研究中具有重要意义。近年来，基于多图谱块的标签融合方法已经取得了许多成功，这些方法通常在图像域中从图谱块字典中表示每个目标块，然后通过在标签域中直接应用估计的表示系数来预测潜在标签。然而，由于这两个域之间的大的差距，在图像域中的估计的表示系数可能不会保持最佳的标签融合。为了克服这一困境，我们提出了一种新的标签融合框架，使加权系数最终是最佳的标签融合逐步构建一个动态的字典在逐层的方式，其中一系列的中间补丁字典逐渐编码的过渡，从图像域中的补丁表示系数的标签融合的最佳权重。我们提出的框架是一般的，以增加标签融合性能的当前国家的最先进的方法。在我们的实验中，我们将我们提出的方法应用于ADNI数据集上的海马分割，并取得了更准确的标记结果，与单层字典的对应方法相比。
Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion. To overcome this dilemma, we propose a novel label fusion framework to make the weighting coefficients eventually to be optimal for the label fusion by progressively constructing a dynamic dictionary in a layer-by-layer manner, where a sequence of intermediate patch dictionaries gradually encode the transition from the patch representation coefficients in image domain to the optimal weights for label fusion. Our proposed framework is general to augment the label fusion performance of the current state-of-the-art methods. In our experiments, we apply our proposed method to hippocampus segmentation on ADNI dataset and achieve more accurate labeling results, compared to the counterpart methods with single-layer dictionary.