Multiseg pipeline: automatic tissue segmentation of brain MR images with subject-specific atlases.
Multiseg pipeline: automatic tissue segmentation of brain MR images with subject-specific atlases.
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多分段管道：使用特定主题图集对大脑 MR 图像进行自动组织分割。
DOI：
10.1117/12.2513237
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Styner,Martin
中科院分区：
文献类型：
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作者：
Pham,Kevin;Yang,Xiao;Niethammer,Marc;Prieto,JuanC;Styner,Martin
由于个体结构变异性，个体脑解剖区域的自动分割和标记具有挑战性。尽管基于图谱的分割已经显示出其用于组织和结构分割的潜力，但是MR外观中的固有自然变异性以及疾病相关变化通常不适当地由单个图谱图像表示。为了具有更准确的表示，在给定的神经成像研究中，可以使用几个地图集用于分割任务。在本文中，我们介绍了MultisegPipeline，它使用多个已经过目视检查的地图集，并捕获新生儿群体的预期变异性。MultisegPipeline使用可变形配准（ANT或QuickSilver可用于此任务）将每个图谱中的标记区域传输到目标图像。另外，使用减少由配准产生的误差的标签融合技术来合并标签集合。最终的输出是一个单一的标签地图，它将所有地图集产生的结果组合成一个共识解决方案。在我们的研究中，使用MultisegPipeline对31名婴儿的脑部MR图像进行分割，并使用留一策略来测试我们的框架。平均骰子得分系数为0.89。
Automated segmentation and labeling of individual brain anatomical regions is challenging due to individual structural variability. Although, atlas-based segmentation has shown its potential for both tissue and structure segmentation, the inherent natural variability as well as disease-related changes in MR appearance is often inappropriately represented by a single atlas image. In order to have a more accurate representation, several atlases may be used for the segmentation task in a given neuroimaging study. In this paper, we present the MultisegPipeline, it uses multiple atlases that have been visually inspected and capture the expected variability in a neonatal population. The MultisegPipeline transfers the labeled regions from each atlas to the target image using deformable registration (ANTs or QuickSilver is available for this task). Additionally, the set of labels are merged using a label fusion technique that reduces the errors produced by the registration. The final output is a single label map that combines the results produced by all atlases into a consensus solution. In our study, the MultisegPipeline is used to segment brain MR images from 31 infants, a leave-one-out strategy was used to test our framework. The average dice score coefficient was 0.89.