Low-Rank Atlas Image Analyses in the Presence of Pathologies.
Low-Rank Atlas Image Analyses in the Presence of Pathologies.
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DOI：
10.1109/tmi.2015.2448556
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发表时间：
2015-12
影响因子：
10.6
通讯作者：
Aylward S
中科院分区：
文献类型：
--
作者：
Liu X;Niethammer M;Kwitt R;Singh N;McCormick M;Aylward S
我们提出了一个共同的框架，用于将图像注册到地图集并形成一个公正的地图集，该地图集可以容忍诸如肿瘤和创伤性脑损伤病变等病理的存在。当不能轻易获得足够数量的健康受试者的协议匹配扫描以形成图谱时，以及当患者的病理导致很大的外观变化时，这个通用框架特别有用。我们的框架结合了低秩加稀疏图像分解技术和迭代、微分同构、分组图像配准方法。在图像配准的每次迭代中，分解技术估计每个图像的“健康”版本作为其低秩分量，并估计每个图像中的病变作为其稀疏分量。每个图像的正常版本用于下一次图像配准迭代。随着图像配准的迭代改进，低秩估计和稀疏估计得到了改进。当该框架应用于图像到地图集的配准时，低秩图像被配准到预定义的地图集，以建立独立于每个图像稀疏分量中的病理的对应关系。最终，图像到地图集的配准可用于定义组织分割的空间先验和跨主题的地图信息。当该框架应用于无偏地图集生成时，在每次迭代中，使用来自患者的低秩图像的平均值作为下一次迭代的地图集图像，直到收敛。由于每次迭代的图谱都是由低秩成分组成的，因此它提供了种群一致的、无病理的外观。利用MICCAI 2012的脑肿瘤分割（BRATS）挑战的合成数据以及模拟和临床肿瘤MRI图像，对所提出的方法进行了评估。
We present a common framework, for registering images to an atlas and for forming an unbiased atlas, that tolerates the presence of pathologies such as tumors and traumatic brain injury lesions. This common framework is particularly useful when a sufficient number of protocol-matched scans from healthy subjects cannot be easily acquired for atlas formation and when the pathologies in a patient cause large appearance changes. Our framework combines a low-rank-plus-sparse image decomposition technique with an iterative, diffeomorphic, group-wise image registration method. At each iteration of image registration, the decomposition technique estimates a “healthy” version of each image as its low-rank component and estimates the pathologies in each image as its sparse component. The healthy version of each image is used for the next iteration of image registration. The low-rank and sparse estimates are refined as the image registrations iteratively improve. When that framework is applied to image-to-atlas registration, the low-rank image is registered to a pre-defined atlas, to establish correspondence that is independent of the pathologies in the sparse component of each image. Ultimately, image-to-atlas registrations can be used to define spatial priors for tissue segmentation and to map information across subjects. When that framework is applied to unbiased atlas formation, at each iteration, the average of the low-rank images from the patients is used as the atlas image for the next iteration, until convergence. Since each iteration’s atlas is comprised of low-rank components, it provides a population-consistent, pathology-free appearance. Evaluations of the proposed methodology are presented using synthetic data as well as simulated and clinical tumor MRI images from the brain tumor segmentation (BRATS) challenge from MICCAI 2012.