低秩来救援——存在病变时基于图谱的分析
Low-rank to the rescue - atlas-based analyses in the presence of pathologies.
作者信息
Liu Xiaoxiao, Niethammer Marc, Kwitt Roland, McCormick Matthew, Aylward Stephen
出版信息
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):97-104. doi: 10.1007/978-3-319-10443-0_13.
Abstract
Low-rank image decomposition has the potential to address a broad range of challenges that routinely occur in clinical practice. Its novelty and utility in the context of atlas-based analysis stems from its ability to handle images containing large pathologies and large deformations. Potential applications include atlas-based tissue segmentation and unbiased atlas building from data containing pathologies. In this paper we present atlas-based tissue segmentation of MRI from patients with large pathologies. Specifically, a healthy brain atlas is registered with the low-rank components from the input MRIs, the low-rank components are then re-computed based on those registrations, and the process is then iteratively repeated. Preliminary evaluations are conducted using the brain tumor segmentation challenge data (BRATS '12).
摘要
低秩图像分解有潜力应对临床实践中经常出现的一系列广泛挑战。它在基于图谱的分析背景下的新颖性和实用性源于其处理包含大病变和大变形图像的能力。潜在应用包括基于图谱的组织分割以及从包含病变的数据构建无偏图谱。在本文中，我们展示了对患有大病变患者的MRI进行基于图谱的组织分割。具体而言，将一个健康脑图谱与输入MRI的低秩分量进行配准，然后基于这些配准重新计算低秩分量，并迭代重复该过程。使用脑肿瘤分割挑战数据（BRATS '12）进行了初步评估。
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