Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models.
Automatic hippocampus segmentation of 7.0 Tesla MR images by combining multiple atlases and auto-context models.
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DOI：
10.1016/j.neuroimage.2013.06.006
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发表时间：
2013-12
期刊：
影响因子：
5.7
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Kim M;Wu G;Li W;Wang L;Son YD;Cho ZH;Shen D
在许多神经科学和临床研究中，海马的精确测量对于揭示个体间的解剖差异或由于衰老或痴呆而引起的个体内细微的纵向变化是非常重要的。尽管已经开发了许多自动分割方法，但是它们的性能仍然受到来自1.5或3.0特斯拉（T）扫描仪的MR图像中海马的差的图像对比度的挑战。随着成像技术的发展，7.0T扫描仪为海马的研究提供了更高的图像对比度和分辨率。然而，由于图像对比度和纹理信息的不同水平，先前开发的用于从1.5T或3.0T图像分割海马的方法不适用于7.0T图像。在本文中，我们提出了一个基于学习的算法自动分割的7.0 T图像的Escherichia campi，利用最先进的多图集框架和自动上下文模型（ACM）的优势。具体而言，ACM在每个图谱域中执行，以通过整合图像外观和局部上下文特征来迭代地构建位置自适应分类器的序列。由于7.0T图像中包含丰富的纹理信息，因此在训练阶段还提取了更高级的纹理特征并将其纳入ACM中。然后，在多图谱分割框架下，为所有图谱训练多个基于ACM的分类器序列，以结合解剖变异性。在应用阶段，对于一个新的图像，它的海马分割可以通过融合的标记结果从所有的地图集，其中每个地图集是通过应用特定的基于ACM的分类器获得。在体素尺寸为0.35 × 0.35 × 0.35 mm 3的20幅7.0 T图像上的实验结果显示，海马分割非常有希望（就Dice重叠比而言为89.1 ± 0.020），这表明对未来的临床和神经科学研究具有很高的适用性。
In many neuroscience and clinical studies, accurate measurement of hippocampus is very important to reveal the inter-subject anatomical differences or the subtle intra-subject longitudinal changes due to aging or dementia. Although many automatic segmentation methods have been developed, their performances are still challenged by the poor image contrast of hippocampus in the MR images acquired especially from 1.5 or 3.0 Tesla (T) scanners. With the recent advance of imaging technology, 7.0 T scanner provides much higher image contrast and resolution for hippocampus study. However, the previous methods developed for segmentation of hippocampus from 1.5 T or 3.0 T images do not work for the 7.0 T images, due to different levels of imaging contrast and texture information. In this paper, we present a learning-based algorithm for automatic segmentation of hippocampi from 7.0 T images, by taking advantages of the state-of-the-art multi-atlas framework and also the auto-context model (ACM). Specifically, ACM is performed in each atlas domain to iteratively construct sequences of location-adaptive classifiers by integrating both image appearance and local context features. Due to the plenty texture information in 7.0 T images, more advanced texture features are also extracted and incorporated into the ACM during the training stage. Then, under the multi-atlas segmentation framework, multiple sequences of ACM-based classifiers are trained for all atlases to incorporate the anatomical variability. In the application stage, for a new image, its hippocampus segmentation can be achieved by fusing the labeling results from all atlases, each of which is obtained by applying the atlas-specific ACM-based classifiers. Experimental results on twenty 7.0 T images with the voxel size of 0.35 × 0.35 × 0.35 mm3 show very promising hippocampus segmentations (in terms of Dice overlap ratio 89.1 ± 0.020), indicating high applicability for the future clinical and neuroscience studies.
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