Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation
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DOI：
10.1016/j.media.2020.101698
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发表时间：
2020-07-01
影响因子：
10.9
通讯作者：
Schirmer, Markus D.
中科院分区：
文献类型：
--
作者：
Dubost, Florian;de Bruijne, Marleen;Schirmer, Markus D.
配准是许多成像流水线的核心组件。在临床扫描的情况下，分辨率较低，有时会出现大量的运动伪影，配准可能会产生较差的结果。在大型临床数据集中对配准质量进行视觉评估是低效的。在这项工作中，我们建议自动评估脑部临床FLAIR MRI扫描中与图谱的配准质量。该方法包括使用神经网络自动分割给定扫描的心室，并将分割与传播到图像空间的寰椎心室进行比较。我们使用所提出的方法来提高临床图像配准到一个一般的图集计算多个注册-一个直接到一般的图集和其他通过不同的年龄特定的图集-然后选择注册，产生最高的心室重叠。最后，作为完整流水线的示例应用，仅使用配准质量高于预定义阈值的扫描来计算白色物质高强度负荷的逐体素图。在超过1000次扫描的单中心数据集中以及包括来自12个中心的142次临床扫描的多中心数据集中对方法进行了评价。自动心室分割在单中心数据集中达到了Dice系数，手动注释为0.89，在多中心数据集中为0.83。与直接配准到一般图谱相比，通过年龄特异性图谱配准可以改善心室重叠（Dice相似系数增加到0.15）。实验还表明，使用配准质量评估方法选择扫描可以提高白色物质高强度负荷的平均图的质量，而不是使用所有扫描来计算白色物质高强度图。在这项工作中，我们证明了一个自动化的工具，用于评估临床扫描中的图像配准质量的实用性。这一图像质量评估步骤最终可以帮助将自动神经成像管道转化为临床。(C)2020爱思唯尔B. V.保留所有权利。
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic. (C) 2020 Elsevier B.V. All rights reserved.