Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta
Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta
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DOI：
10.1111/cgf.12911
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发表时间：
2016-06-01
影响因子：
2.5
通讯作者：
Lawonn, K.
中科院分区：
文献类型：
--
作者：
Meuschke, M.;Koehler, B.;Lawonn, K.
我们提出了主动脉涡流分类 (AVOCLA)，它允许对人类主动脉中的涡流进行半自动分类。目前的医学研究假设心血管疾病与涡流等血流模式之间存在密切关系。此类涡流是根据特定的、非标准化的属性提取并手动分类的。我们采用凝聚层次聚类对代表涡流的路径线进行分组，作为后续分类的基础。类别基于涡流的大小、方向和形状、其相对于心动周期的时间发生以及其相对于血管路线的空间位置。分类结果通过 2D 和 3D 可视化技术呈现。为了确认这两种方法的有用性，我们报告了用户研究的结果。此外，AVOCLA 还应用于 15 个健康志愿者和患有不同心血管疾病的患者的数据集。考虑涡数和五个特定属性，将半自动分类的结果与两位领域专家手动生成的地面实况进行定性比较。
We present an Aortic Vortex Classification (AVOCLA) that allows to classify vortices in the human aorta semi-automatically. Current medical studies assume a strong relation between cardiovascular diseases and blood flow patterns such as vortices. Such vortices are extracted and manually classified according to specific, unstandardized properties. We employ an agglomerative hierarchical clustering to group vortex-representing path lines as basis for the subsequent classification. Classes are based on the vortex' size, orientation and shape, its temporal occurrence relative to the cardiac cycle as well as its spatial position relative to the vessel course. The classification results are presented by a 2D and 3D visualization technique. To confirm the usefulness of both approaches, we report on the results of a user study. Moreover, AVOCLA was applied to 15 datasets of healthy volunteers and patients with different cardiovascular diseases. The results of the semi-automatic classification were qualitatively compared to a manually generated ground truth of two domain experts considering the vortex number and five specific properties.