Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts
Segmentation of interwoven 3d tubular tree structures utilizing shape priors and graph cuts
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DOI：
10.1016/j.media.2009.11.003
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发表时间：
2010-04-01
影响因子：
10.9
通讯作者：
Beichel, Reinhard
中科院分区：
文献类型：
--
作者：
Bauer, Christian;Pock, Thomas;Beichel, Reinhard
管状树结构的分割，如血管系统的体积数据集是至关重要的许多医学应用。我们提出了一种新的方法，允许同时分离和分割多个交织的管状树结构。该算法包括两个主要的处理步骤。首先，树结构的识别和相应的形状先验是通过使用自下而上的管状对象的识别与自上而下的这些对象到完整的树结构的分组相结合而产生的。分组步骤允许我们分离交织的树并处理局部干扰。其次，所产生的形状先验被用于不同的管状系统的内在分割，以避免泄漏或欠分割局部扰动区域。我们已经在体模和不同的临床CT数据集上评估了我们的方法，并证明了其正确获得/分离不同树结构、准确确定管状树结构的表面以及鲁棒地处理噪声、干扰（例如，肿瘤），以及偏离圆柱形管形状，例如动脉瘤。(C)2009爱思唯尔有限公司版权所有。
The segmentation of tubular tree structures like vessel systems in volumetric datasets is of vital interest for many medical applications. We present a novel approach that allows to simultaneously separate and segment multiple interwoven tubular tree structures. The algorithm consists of two main processing steps. First, the tree structures are identified and corresponding shape priors are generated by using a bottom-up identification of tubular objects combined with a top-down grouping of these objects into complete tree structures. The grouping step allows us to separate interwoven trees and to handle local disturbances. Second, the generated shape priors are utilized for the intrinsic segmentation of the different tubular systems to avoid leakage or undersegmentation in locally disturbed regions. We have evaluated our method on phantom and different clinical CT datasets and demonstrated its ability to correctly obtain/separate different tree structures, accurately determine the surface of tubular tree structures, and robustly handle noise, disturbances (e.g., tumors), and deviations from cylindrical tube shapes like for example aneurysms. (C) 2009 Elsevier B.V. All rights reserved.