SKULL-STRIPPING WITH DEFORMABLE ORGANISMS.
SKULL-STRIPPING WITH DEFORMABLE ORGANISMS.
复制标题
用可变形生物剥去头骨。
DOI：
10.1109/isbi.2011.5872723
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Terzopoulos,Demetri
中科院分区：
文献类型：
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作者：
Prasad,Gautam;Joshi,AnandA;Thompson,PaulM;Toga,ArthurW;Shattuck,DavidW;Terzopoulos,Demetri
在人体头部的磁共振（MR）图像中将大脑与非脑组织分割，也称为颅骨剥离，是神经成像数据分析中的关键处理步骤。虽然已经开发了许多算法来解决这个问题，但挑战仍然存在。在本文中，我们将“可变形有机体”框架应用于头骨剥离问题。在这个框架内，可变形模型配备了基于人工生命原理的更高级别的控制机制，包括感知、反应行为、知识表示和主动规划。我们的新可变形生物体由一个高级计划管理，该计划旨在对MR图像中头部的各个部分进行全自动分割，并且它们能够合作计算鲁棒和准确的分割。我们将我们的分割方法应用于人类MRI数据的测试集，使用数据的手动描绘作为参考“金标准”。我们将这些结果与使用集合相似性度量的三种广泛使用的方法的结果进行比较。
Segmenting brain from non-brain tissue within magnetic resonance (MR) images of the human head, also known as skull-stripping, is a critical processing step in the analysis of neuroimaging data. Though many algorithms have been developed to address this problem, challenges remain. In this paper, we apply the “deformable organism” framework to the skull-stripping problem. Within this framework, deformable models are equipped with higher-level control mechanisms based on the principles of artificial life, including sensing, reactive behavior, knowledge representation, and proactive planning. Our new deformable organisms are governed by a high-level plan aimed at the fully-automated segmentation of various parts of the head in MR imagery, and they are able to cooperate in computing a robust and accurate segmentation. We applied our segmentation approach to a test set of human MRI data using manual delineations of the data as a reference “gold standard.” We compare these results with results from three widely used methods using set-similarity metrics.