Centre for Medical Image Computing, University College London, London, United Kingdom.
PLoS One. 2013 Aug 2;8(8):e70059. doi: 10.1371/journal.pone.0070059. Print 2013.
Multi-atlas segmentation has been widely used to segment various anatomical structures. The success of this technique partly relies on the selection of atlases that are best mapped to a new target image after registration. Recently, manifold learning has been proposed as a method for atlas selection. Each manifold learning technique seeks to optimize a unique objective function. Therefore, different techniques produce different embeddings even when applied to the same data set. Previous studies used a single technique in their method and gave no reason for the choice of the manifold learning technique employed nor the theoretical grounds for the choice of the manifold parameters. In this study, we compare side-by-side the results given by 3 manifold learning techniques (Isomap, Laplacian Eigenmaps and Locally Linear Embedding) on the same data set. We assess the ability of those 3 different techniques to select the best atlases to combine in the framework of multi-atlas segmentation. First, a leave-one-out experiment is used to optimize our method on a set of 110 manually segmented atlases of hippocampi and find the manifold learning technique and associated manifold parameters that give the best segmentation accuracy. Then, the optimal parameters are used to automatically segment 30 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). For our dataset, the selection of atlases with Locally Linear Embedding gives the best results. Our findings show that selection of atlases with manifold learning leads to segmentation accuracy close to or significantly higher than the state-of-the-art method and that accuracy can be increased by fine tuning the manifold learning process.
多图谱分割已被广泛用于分割各种解剖结构。该技术的成功部分依赖于图谱的选择，这些图谱在经过配准后可以最佳地映射到新的目标图像。最近，流形学习已被提议作为图谱选择的一种方法。每种流形学习技术都试图优化一个独特的目标函数。因此，即使应用于相同的数据集，不同的技术也会产生不同的嵌入。以前的研究在其方法中使用了单一技术，既没有说明选择流形学习技术的原因，也没有为选择流形参数提供理论依据。在这项研究中，我们并排比较了 3 种流形学习技术（Isomap、Laplacian Eigenmaps 和局部线性嵌入）在同一数据集上的结果。我们评估了这 3 种不同技术在多图谱分割框架中选择最佳图谱进行组合的能力。首先，通过对 110 个手动分割的海马图谱进行的一次留一实验来优化我们的方法，并找到能够给出最佳分割精度的流形学习技术和相关流形参数。然后，使用最优参数自动分割来自阿尔茨海默病神经影像学倡议（ADNI）的 30 个受试者。对于我们的数据集，使用局部线性嵌入选择图谱可以得到最佳的结果。我们的研究结果表明，使用流形学习选择图谱可以达到或显著高于最新方法的分割精度，并且可以通过微调流形学习过程来提高精度。
