STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation
STEPS: Similarity and Truth Estimation for Propagated Segmentations and its application to hippocampal segmentation and brain parcelation
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DOI：
10.1016/j.media.2013.02.006
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发表时间：
2013-08-01
影响因子：
10.9
通讯作者：
Ourselin, Sebastien
中科院分区：
文献类型：
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作者：
Cardoso, M. Jorge;Leung, Kelvin;Ourselin, Sebastien
感兴趣结构的解剖分割对于医学成像中的定量分析至关重要。几个自动化的多图集为基础的分割传播方法，利用人工描绘从多个模板出现有前途的。然而，在诊断或临床试验中需要高水平的准确性和可靠性。我们提出了一种新的局部排序策略的模板选择的基础上的局部归一化互相关（LNCC）和扩展的经典STAPLE算法由沃菲尔德等人。（2004年），我们称之为步骤的相似性和真理估计的分割。它解决了众所周知的局部与全局图像匹配的问题，以及由于结构尺寸而在性能估计中引入的偏差。我们评估了海马分割使用留一交叉验证与优化的模型参数的方法; STEPS实现了平均骰子得分为0.925时，与手动分割。与其他最先进的融合技术相比，这在分割精度方面明显更好。此外，由于更精细的解剖尺度，即使仅使用三分之一的模板，STEPS也可以获得更准确的分割，减少对大型模板数据库的依赖。使用来自不同MRI成像系统和协议的阿尔茨海默病神经成像倡议（ADNI）扫描的子集，STEPS产生了类似的准确分割（Dice = 0.903）。在ADNI数据库上进行横截面和纵向海马体积研究。对照组海马体积（mm（3））的平均值为5195 ± 656; MCI组为4786 ± 781;阿尔茨海默病组为4427 ± 903;海马萎缩率（%/年）分别为1.09 ± 3.0、2.74 ± 3.5和4.04 ± 3.6。两个疾病组之间海马体积和体积变化率存在统计学显著性差异（p < 10（-3））。最后，STEPS也被应用在一个多标签分割传播的情况下，使用留一交叉验证，以包裹83个单独的结构的大脑。STEPS与最先进的多标签融合算法的比较显示，在几个关键结构中，分割准确性在统计学上显著提高（p < 10（-4））。(C)2013爱思唯尔有限公司版权所有。
Anatomical segmentation of structures of interest is critical to quantitative analysis in medical imaging. Several automated multi-atlas based segmentation propagation methods that utilise manual delineations from multiple templates appear promising. However, high levels of accuracy and reliability are needed for use in diagnosis or in clinical trials. We propose a new local ranking strategy for template selection based on the locally normalised cross correlation (LNCC) and an extension to the classical STAPLE algorithm by Warfield et al. (2004), which we refer to as STEPS for Similarity and Truth Estimation for Propagated Segmentations. It addresses the well-known problems of local vs. global image matching and the bias introduced in the performance estimation due to structure size. We assessed the method on hippocampal segmentation using a leave-one-out cross validation with optimised model parameters; STEPS achieved a mean Dice score of 0.925 when compared with manual segmentation. This was significantly better in terms of segmentation accuracy when compared to other state-of-the-art fusion techniques. Furthermore, due to the finer anatomical scale, STEPS also obtains more accurate segmentations even when using only a third of the templates, reducing the dependence on large template databases. Using a subset of Alzheimer's Disease Neuroimaging Initiative (ADNI) scans from different MRI imaging systems and protocols, STEPS yielded similarly accurate segmentations (Dice = 0.903). A cross-sectional and longitudinal hippocampal volumetric study was performed on the ADNI database. Mean +/- SD hippocampal volume (mm(3)) was 5195 +/- 656 for controls; 4786 +/- 781 for MCI; and 4427 +/- 903 for Alzheimer's disease patients and hippocampal atrophy rates (%/year) of 1.09 +/- 3.0, 2.74 +/- 3.5 and 4.04 +/- 3.6 respectively. Statistically significant (p < 10(-3)) differences were found between disease groups for both hippocampal volume and volume change rates. Finally, STEPS was also applied in a multi-label segmentation propagation scenario using a leave-one-out cross validation, in order to parcellate 83 separate structures of the brain. Comparisons of STEPS with state-of-the-art multi-label fusion algorithms showed statistically significant segmentation accuracy improvements (p < 10(-4)) in several key structures. (C) 2013 Elsevier B.V. All rights reserved.