Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning

Ivantsits, Matthias; Huellebrand, Markus; Kelle, Sebastian; Küehne, Titus; Hennemuth, Anja

doi:10.1007/978-3-030-72862-5_10

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…The KNN model shows a good performance in the classification of aneurysm rupture with a mean AUC of 0.811 on the test set. Ivantsits et al (2021) and Liu et al (2021), respectively, proposed two excellent semiautomatic aneurysm rupture risk estimation methods on the CADA dataset. Table 3 shows the comparison on the metrics of our approach with two related works.…”

Section: Rupture Risk Estimation Resultsmentioning

confidence: 99%

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Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

Yang

et al. 2022

Front. Neurosci.

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The rupture of aneurysms is the main cause of spontaneous subarachnoid hemorrhage (SAH), which is a serious life-threatening disease with high mortality and permanent disability rates. Therefore, it is highly desirable to evaluate the rupture risk of aneurysms. In this study, we proposed a novel semiautomatic prediction model for the rupture risk estimation of aneurysms based on the CADA dataset, including 108 datasets with 125 annotated aneurysms. The model consisted of multidimensional feature fusion, feature selection, and the construction of classification methods. For the multidimensional feature fusion, we extracted four kinds of features and combined them into the feature set, including morphological features, radiomics features, clinical features, and deep learning features. Specifically, we applied the feature extractor 3D EfficientNet-B0 to extract and analyze the classification capabilities of three different deep learning features, namely, no-sigmoid features, sigmoid features, and binarization features. In the experiment, we constructed five distinct classification models, among which the k-nearest neighbor classifier showed the best performance for aneurysm rupture risk estimation, reaching an F2-score of 0.789. Our results suggest that the full use of multidimensional feature fusion can improve the performance of aneurysm rupture risk assessment. Compared with other methods, our method achieves the state-of-the-art performance for aneurysm rupture risk assessment methods based on CADA 2020.

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Section: Rupture Risk Estimation Resultsmentioning

confidence: 99%

“… Ivantsits et al (2021) and Liu et al (2021) , respectively, proposed two excellent semiautomatic aneurysm rupture risk estimation methods on the CADA dataset. Table 3 shows the comparison on the metrics of our approach with two related works.…”

Section: Resultsmentioning

confidence: 99%

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

Yang

et al. 2022

Front. Neurosci.

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“…Invantsits et al [20] used machine learning on morphological features, gray-level radiomics features and features based on graphs to predict aneurysm rupture. They constructed a directed graph from the carotid or vertebral artery to the aneurysm.…”

Section: Rupture Riskmentioning

confidence: 99%

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management

et al. 2023

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Purpose Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with multiple aneurysms. Clinical research proposed more advanced analysis of intracranial aneurysm, but requires many complex preprocessing steps. Advanced tools for automatic aneurysm analysis are needed to transfer current research into clinical routine. Methods We propose a pipeline for intracranial aneurysm analysis using deep learning-based mesh segmentation, automatic centerline and outlet detection and automatic generation of a semantic vessel graph. We use the semantic vessel graph for morphological analysis and an automatic rupture state classification. Results The deep learning-based mesh segmentation can be successfully applied to aneurysm surface meshes. With the subsequent semantic graph extraction, additional morphological parameters can be extracted that take the whole vascular domain into account. The vessels near ruptured aneurysms had a slightly higher average torsion and curvature compared to vessels near unruptured aneurysms. The 3D surface models can be further employed for rupture state classification which achieves an accuracy of 83.3%. Conclusion The presented pipeline addresses several aspects of current research and can be used for aneurysm analysis with minimal user effort. The semantic graph representation with automatic separation of the aneurysm from the parent vessel is advantageous for morphological and hemodynamical parameter extraction and has great potential for deep learning-based rupture state classification.

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Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

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Rupture of an intracranial aneurysm often results in subarachnoid hemorrhage, a life-threatening condition with high mortality and morbidity. The Cerebral Aneurysm Detection and Analysis (CADA) competition was organized to support the development and benchmarking of algorithms for the detection, analysis, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. Segmentation quality was measured using Jaccard and a combination of different surface distance measurements. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADAchallenge. The detection solutions presented by the community are mostly accurate (F2 score 0.92) with a small number of missed aneurysms with diameters of 3.5 mm. In addition, the delineation of these structures is very good with a Jaccard score of 0.915. The rupture risk estimation methods achieved an F2 score of 0.7. The performance of the detection and segmentation solutions is equivalent to that of human experts. In rupture risk estimation, the best results are obtained by combining different image-based, morphological and computational fluid dynamic parameters using machine learning methods.

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Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning

Cited by 3 publications

References 28 publications

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

Intracranial Aneurysm Rupture Risk Estimation With Multidimensional Feature Fusion

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

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