by Monique Meuschke, Benjamin Köhler, Uta Preim, Bernhard Preim, Kai Lawonn
Abstract:
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.
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Reference:
Semi-automatic Vortex Flow Classification in 4D PC-MRI Data of the Aorta (Monique Meuschke, Benjamin Köhler, Uta Preim, Bernhard Preim, Kai Lawonn), In Computer Graphics Forum, volume 35, 2016.
Bibtex Entry:
@article{meuschke_semi-automatic_2016,
	title = {Semi-automatic {Vortex} {Flow} {Classification} in 4D {PC}-{MRI} {Data} of the {Aorta}},
	volume = {35},
	issn = {1467-8659},
	url = {http://dx.doi.org/10.1111/cgf.12911},
	doi = {10.1111/cgf.12911},
	abstract = {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.},
	number = {3},
	journal = {Computer Graphics Forum},
	author = {Meuschke, Monique and Köhler, Benjamin and Preim, Uta and Preim, Bernhard and Lawonn, Kai},
	year = {2016},
	keywords = {Categories and Subject Descriptors (according to ACM CCS), I.4.9 [Computer Graphics]: Image Processing and Computer Vision—Applications},
	pages = {351--360}
}