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Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning

机译：通过SIMPLE上下文学习在临床获得的CT上进行高效的多图腹部分割

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Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining. (C) 2015 Elsevier B.V. All rights reserved.
机译：考虑到人体腹部之间的差异以及器官之间复杂的3-D关系，临床上获得的计算机断层扫描（CT）上的腹部分割一直是一个具有挑战性的问题。多图集分割（MAS）通过通过图像配准和统计融合利用标签图集，提供了潜在的强大解决方案。我们认为，在大量注册错误的背景下，图集选择的效率需要进一步探索。性能水平评估的选择性和迭代方法（SIMPLE）是一种结合了图谱选择和标签融合的MAS技术，已被证明对前列腺放射治疗计划有效。在本文中，我们将回顾使用临床获得的CT来分割12个腹部结构的地图集选择和融合技术。使用重新派生的SIMPLE算法，我们表明可以通过通过贝叶斯先验考虑外源信息（所谓的上下文学习）来提高多器官分类的性能。这些创新与联合标签融合（JLF）方法集成在一起，以减少每个器官的选定图集之间相关错误的影响，并且使用图谱切割技术来规范化组合分割。在对100名受试者的研究中，提出的方法优于其他类似的MAS方法，包括多数表决，SIMPLE，JLF和Wolz局部加权表决技术。所提出的技术提供了相对于最新技术的持续改进（DSC分别比JLF和Wolz分别提高了7.0％和16.2％），并朝着有效分割大规模临床获得的CT数据进行生物标志物筛选的方向发展，手术导航和数据挖掘。 （C）2015 Elsevier B.V.保留所有权利。

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