Learning to rank atlases for multiple-atlas segmentation.
Learning to rank atlases for multiple-atlas segmentation.
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DOI：
10.1109/tmi.2014.2327516
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发表时间：
2014-10
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Sanroma G;Wu G;Gao Y;Shen D
近年来，多图谱分割（MAS）在医学成像领域取得了巨大成功。关键假设是多个图集比单个图集更有可能正确标记目标图像。然而，图谱选择问题仍未得到探索。传统上，图像相似度用于选择一组图集。不幸的是，这种启发式标准不一定与最终的分割性能相关。为了解决这个看似简单但关键的问题，我们提出了一种基于学习的图集选择方法来挑选最佳图集，从而实现更准确的分割。我们的主要想法是学习观察到的实例（即一对图集和目标图像）的成对外观与其最终标记性能（例如，使用 Dice 比率）之间的关系。通过这种方式，我们根据预期的标注精度选择最好的图集。我们的图集选择方法足够通用，可以与任何现有的 MAS 方法集成。我们在 ADNI、SATA、IXI 和 LONI LPBA40 数据集的广泛实验评估中展示了我们的图集选择方法的优势。如实验所示，我们的方法可以提高三种广泛使用的 MAS 方法的性能，优于其他基于学习和基于图像相似性的图集选择方法。
Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.