Rupture Status Classification of Intracranial Aneurysms Using Morphological Parameters

Niemann, Uli; Berg, Philipp; Niemann, Annika; Beuing, Oliver; Preim, Bernhard; Spiliopoulou, Myra; Saalfeld, Sylvia

doi:10.1109/CBMS.2018.00016

by Uli Niemann, Philipp Berg, Annika Niemann, Oliver Beuing, Bernhard Preim, Myra Spiliopoulou, Sylvia Saalfeld

Abstract:

Intracranial aneurysms are pathologic dilations of the vessel wall, which bear the risk of rupture and of fatal consequences for the patient. Since treatment may be accompanied by severe complications as well, rupture risk assessment and thus rupture risk prediction plays an important role in clinical research. In this work, we investigate the potential of morphological features for rupture risk status classification in 100 intracranial aneurysms. We propose a pipeline for morphological feature extraction and rupture status classification with subsequent feature ranking and inspection. Our classification setup involves training separate models for each aneurysm type (sidewall or bifurcation) with multiple learning algorithms. We report on the classification performance of our pipeline and examine the predictive power of each morphological parameter towards rupture status classification. Further, we identify the most important features for the best models and study their marginal prediction.

Reference:

Rupture Status Classification of Intracranial Aneurysms Using Morphological Parameters (Uli Niemann, Philipp Berg, Annika Niemann, Oliver Beuing, Bernhard Preim, Myra Spiliopoulou, Sylvia Saalfeld), In 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS), 2018.

Bibtex Entry:

@inproceedings{niemann_rupture_2018,
	address = {Karlstad, Sweden},
	title = {Rupture {Status} {Classification} of {Intracranial} {Aneurysms} {Using} {Morphological} {Parameters}},
	doi = {10.1109/CBMS.2018.00016},
	abstract = {Intracranial aneurysms are pathologic dilations of the vessel wall, which bear the risk of rupture and of fatal consequences for the patient. Since treatment may be accompanied by severe complications as well, rupture risk assessment and thus rupture risk prediction plays an important role in clinical research. In this work, we investigate the potential of morphological features for rupture risk status classification in 100 intracranial aneurysms. We propose a pipeline for morphological feature extraction and rupture status classification with subsequent feature ranking and inspection. Our classification setup involves training separate models for each aneurysm type (sidewall or bifurcation) with multiple learning algorithms. We report on the classification performance of our pipeline and examine the predictive power of each morphological parameter towards rupture status classification. Further, we identify the most important features for the best models and study their marginal prediction.},
	booktitle = {2018 {IEEE} 31st {International} {Symposium} on {Computer}-{Based} {Medical} {Systems} ({CBMS})},
	author = {Niemann, Uli and Berg, Philipp and Niemann, Annika and Beuing, Oliver and Preim, Bernhard and Spiliopoulou, Myra and Saalfeld, Sylvia},
	month = jun,
	year = {2018},
	keywords = {aneurysm, Bifurcation, Blood vessels, feature extraction, fracture, Intracranial aneurysm, intracranial aneurysms, learning (artificial intelligence), medical disorders, Medical Image Analysis, medical image processing, morphological feature extraction, Morphological parameters, Neck, pathologic dilation, patient treatment, Pipelines, Predictive models, Rupture risk assessment, rupture risk status classification, Rupture Status Classification, Surface morphology, vessel wall},
	pages = {48--53}
}