Weighted atlas auto-context with application to multiple organ segmentation
Weighted atlas auto-context with application to multiple organ segmentation
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DOI：
10.1109/wacv.2016.7477605
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发表时间：
2016-03
期刊：
影响因子：
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通讯作者：
Telmo Amaral;I. Kyriazakis;S. McKenna;T. Plötz
中科院分区：
文献类型：
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作者：
Telmo Amaral;I. Kyriazakis;S. McKenna;T. Plötz
由二维图像中表示的多个非刚性部分形成的三维对象的分割可能会产生困难。涉及其空间布置受到弱限制的部分的问题，以及其外观和形式在图像中变化的问题，可能特别具有挑战性。考虑空间上下文信息的分割方法已经解决了这些类型的问题，这些问题通常涉及多模态性质的图像数据。自动上下文（AC）技术的一个有吸引力的特征是，通常通过平均由专家创建的多个标签图获得的先前的“图集”可以用作上下文数据的初始源。然而，以这种方式获得的先验很可能隐藏数据固有的多模态。我们提出了一个修改AC中的概率地图集的部分位置迭代改进，并提供作为一个额外的信息源。我们说明了这种技术与分割猪内脏图像中的单个器官的问题，报告统计学上的显着改进，传统的AC和一个国家的最先进的技术的基础上的条件随机场。
Difficulties can arise from the segmentation of three-dimensional objects formed by multiple non-rigid parts represented in two-dimensional images. Problems involving parts whose spatial arrangement is subject to weak restrictions, and whose appearance and form change across images, can be particularly challenging. Segmentation methods that take into account spatial context information have addressed these types of problem, which often involve image data of a multi-modal nature. An attractive feature of the auto-context (AC) technique is that a prior "atlas", typically obtained by averaging multiple label maps created by experts, can be used as an initial source of contextual data. However, a prior obtained in this way is likely to hide the inherent multi-modality of the data. We propose a modification of AC in which a probabilistic atlas of part locations is iteratively improved and made available as an additional source of information. We illustrate this technique with the problem of segmenting individual organs in images of pig offal, reporting statistically significant improvements in relation to both conventional AC and a state-of-the-art technique based on conditional random fields.