MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.
MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection.
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DOI：
10.1016/j.neuroimage.2015.11.073
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发表时间：
2016-02-15
期刊：
影响因子：
5.7
通讯作者：
Alzheimer's Neuroimaging Initiative
中科院分区：
文献类型：
--
作者：
Doshi J;Erus G;Ou Y;Resnick SM;Gur RC;Gur RE;Satterthwaite TD;Furth S;Davatzikos C;Alzheimer's Neuroimaging Initiative
基于神经网络的自动解剖标记是医学图像分割中的基本工具，因为它定义了随后分析结构和功能图像数据的感兴趣区域。在过去的5年或更长时间里，对多图谱扭曲和融合技术的广泛研究已经清楚地证明了基于共识的分割的优势。然而，常见的方法是使用具有单个配准方法和参数集的多个图谱，这对于每个单独的扫描、解剖区域和问题/数据类型来说不一定是最佳的。不同的登记标准和参数组产生不同的解决办法，每种办法都提供补充信息。在这里，我们提出了一个共识标记框架，通过使用几个扭曲算法，正则化参数和地图集，在目标图像空间中生成一个广泛的标记地图集的合奏。标签融合集成了两个互补的信息源：一个本地相似性排名，以选择本地最佳的图集和一个边界调制项，以完善分割一致的目标图像的强度分布。集成方法始终优于单独使用单个扭曲方法的分割，在几个基准数据集上实现了高精度。MUSE方法已被用于处理来自各种数据集的数千次扫描，产生了稳健和一致的结果。MUSE作为可下载的软件包和可在CBICA图像处理门户网站（https：//www.example.com）上运行的应用程序公开提供，CBICA图像处理门户网站是一个用于远程处理医学图像的基于Web的平台。ipp.cbica.upenn.edu
Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images.