Abstract
Intracranial aneurysms frequently cause subarachnoid hemorrhage—a life-threatening condition with a high mortality and morbidity rate. State-of-the-art methods of the rupture risk prediction combine demographic, clinical, morphological, and computational fluid dynamics based hemodynamic parameters. We propose a method of blending morphological features, computational fluid dynamics parameters, and patient demographic features. The shape and wall-shear-stress at each point of the aneurysm are encoded with a deep point cloud neural network and extended by additional location encodings of the aneurysm as well as age and sex of the patient. On this concatenated feature vector, an MLP infers the probability of rupture for a given cerebral aneurysm. The proposed network was trained on the CADA - rupture risk estimation challenge set of 109 aneurysms. The proposed method achieves an accuracy of 0.64 and an F2-score of 0.73 on the private CADA - rupture risk estimation challenge test set of 30 aneurysms.
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References
Teunissen, L.L., et al.: Risk Factors for Subarachnoid Hemorrhage (1996)
Can, A., et al.: Association of intracranial aneurysm rupture with smoking duration, intensity, and cessation (2017)
Chabert, S., et al.: Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture (2017)
Detmer, F.J., et al.: Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics (2019)
Cebral, J.R.: Analysis of hemodynamics and wall mechanics at sites of cerebral aneurysm rupture (2015)
Detmer, F.J., et al.: Associations of hemodynamics, morphology, and patient characteristics with aneurysm rupture stratified by aneurysm location (2019)
Thompson, B.G., et al.: Guidelines for the Management of Patients With Unruptured Intracranial Aneurysms A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association (2015)
Lindgren, A.E., et al.: Irregular shape of intracranial aneurysm indicates rupture risk irrespective of size in a population-based cohort (2016)
Tanioka, S., et al.: Machine learning classification of cerebral aneurysm rupture status with morphologic variables and hemodynamic parameters (2020)
Paliwal, N., et al.: Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning (2018)
Xiang, J., et al.: Hemodynamic–morphologic discriminants for intracranial aneurysm rupture (2011)
Suzuki, M., et al.: Classification model for cerebral aneurysm rupture prediction using medical and blood-flow-simulation data (2019)
Chen, G., et al.: Development and validation of machine learning prediction model based on computed tomography angiography-derived hemodynamics for rupture status of intracranial aneurysms: a Chinese multicenter study (2020)
Kleinloog, R., et al.: Risk factors for intracranial aneurysm rupture: a systematic review (2018)
Chandra, A.R., et al.: Initial study of the radiomics of intracranial aneurysms using Angiographic Parametric Imaging (API) to evaluate contrast flow changes (2019)
Podgorsak, A.R., et al.: Automatic radiomic feature extraction using deep learning for angiographic parametric imaging of intracranial aneurysms (2020)
Liu, Q., et al.: Bifurcation configuration is an independent risk factor for aneurysm rupture irrespective of location (2019)
Liu, Q., et al.: Prediction of aneurysm stability using a machine learning model based on pyradiomics-derived morphological features (2019)
Juchler, N., et al.: Radiomics approach to quantify shape irregularity from crowd-based qualitative assessment of intracranial aneurysms (2020)
Yang, L., Chakraborty, R.: A GMM based algorithm to generate point-cloud and its application to neuroimaging (2019)
Gutierrez-Becker, B., Wachinger, C.: Deep multi-structural shape analysis: application to neuroanatomy (2018)
Wang, Y., Sun, Y., Liu, Z., Sarma, S., Bronstein, M., Solomon, J.: Dynamic graph CNN for learning on point clouds (2018)
Ruizhongtai Qi, C., Su, H., Mo, K., Guibas, L.: PointNet: deep learning on point sets for 3D classification and segmentation (2016)
CADA rupture risk estimation challenge. https://cada-rre.grand-challenge.org/. Accessed 05 Oct 2020
AneuRisk dataset. http://ecm2.mathcs.emory.edu/aneuriskweb/repository. Accessed 26 Nov 2020
Aneux dataset. https://www.aneux.ch/home/internal/. Accessed 26 Nov 2020
Database of Cerebral Artery Geometries including Aneurysms at the Middle Cerebral Artery Bifurcation. https://figshare.shef.ac.uk/articles/dataset/Database_of_Cerebral_Artery_Geometries_including_Aneurysms_at_the_Middle_Cerebral_Artery_Bifurcation/4806910/1. Accessed 26 Nov 2020
Goubergrits, L., et al.: In vitro study of near-wall flow in a cerebral aneurysm model with and without coils (2010)
Wellnhofer, E., Osman, J., Kertzscher, U., Affeld, K., Fleck, E., Goubergrits, L.: Flow simulation studies in coronary arteries—impact of side-branches (2010)
Scheel, P., Ruge, Ch., Petruch, U.R., Schoening, M.: Color duplex measurement of cerebral blood flow volume in healthy adults (2000)
Kato, T., Indo, T., Yoshida, E., Iwasaki, Y., Sone, M., Sobue, G.: Contrast-enhanced 2D cine Phase MR angiography for measurement of basilar artery blood flow in posterior circulation ischemia (2002)
Cebral, J.R., Castro, M.A., Putman, C.M., Alperin, N.: Flow–area relationship in internal carotid and vertebral arteries (2008)
Goubergrits, L., Schaller, J., Kertzscher, U., Woelken, T., Ringelstein, M., Spuler, A.: Hemodynamic impact of cerebral aneurysm endovascular treatment devices: coils and flow diverters (2014)
Berg, P., et al.: Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)—phase II: rupture risk assessment (2019)
Wermer, M.J.H., van der Schaaf, I.C., Algra, A., Rinkel, G.J.E.: Risk of rupture of unruptured intracranial aneurysms in relation to patient and aneurysm characteristics (2007)
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Ivantsits, M., Goubergrits, L., Brüning, J., Spuler, A., Hennemuth, A. (2021). Intracranial Aneurysm Rupture Prediction with Computational Fluid Dynamics Point Clouds. In: Hennemuth, A., Goubergrits, L., Ivantsits, M., Kuhnigk, JM. (eds) Cerebral Aneurysm Detection and Analysis. CADA 2020. Lecture Notes in Computer Science(), vol 12643. Springer, Cham. https://doi.org/10.1007/978-3-030-72862-5_11
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DOI: https://doi.org/10.1007/978-3-030-72862-5_11
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