Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Timmins, Kimberley M.; Kuijf, Hugo J.; Vergouwen, Mervyn D.I.; Ruigrok, Ynte M.; Velthuis, Birgitta K.; Schaaf, Irene C. van der

doi:10.3174/ajnr.a7418

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…This suggests that it is the topology of the aneurysm surface itself which is high indicative of growth or stability as opposed to UIA configuration relative to parent vessels. This result is also on par with previous studies, where measurements of just the UIA were used for distinguishing between growing and stable UIAs [34,150,154]. However, it is also worth noting that using a ROI as opposed to the UIA mesh does not greatly reduce the performance of the method.…”

Section: Discussionsupporting

confidence: 82%

“…As computer aided radiology tools continue to be developed, there is the possibility to measure UIAs in 3D, including their shape [147]. Quantitative shape/morphology measures of UIAs could be used, including to distinguish between growing and stable aneurysms [34,150,154]. Based on such morphological parameters, as well as classical parameters, UIA rupture risk prediction models have been developed [155,156].…”

Section: Introductionmentioning

confidence: 99%

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Image Analysis and Deep Learning Techniques for the Detection and Characterisation of Unruptured Intracranial Aneurysms

Timmins¹

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This thesis presents image analysis techniques for the detection and characterisation of unruptured intracranial aneurysms (UIAs). Using such methods, robust and reliable growth and rupture assessment of UIAs can be made to aid in treatment decision making. The first half of this thesis consider automatic UIA detection and segmentation methods from angiographic scans (TOF-MRAs and CTAs). Chapter 2 describes the organisation of an international biomedical image analysis challenge. Teams submitted automatic UIA detection and segmentation methods, which were evaluated on a held-out test set. The winning detection method has since been developed into an open-source framework for medical image detection (nnDetection). The challenge remains as an important benchmark for UIA detection and segmentation methods. Chapter 3 describes an anomaly detection method using a variational autoencoder (VAE) trained on healthy TOF-MRAs. Reconstructed TOF-MRAs with diagnosed aneurysms had a lower Structural Similarity Index (SSIM), than TOF-MRAs of subjects with no aneurysms. Importantly, the results identified that structure and shape within the scans, and not just intensity, is important for UIA detection. The UIA detection method in Chapter 4 exploits the fact surface of a UIA is different from surrounding vessels. Vessels were segmented from TOF-MRAs and meshes were fitted to the surface. A mesh convolutional neural network was trained using the labelled vessel meshes, to detect UIAs. The modality-independent method has comparable performance for both TOF-MRAs and CTAs. Automatic UIA detection and segmentation described in Chapters 2-4 allow automatic 3D volume and morphology UIA measurements for potential growth and rupture risk assessment. Chapters 5 and 6 investigate UIA volume and morphology and their relationship to UIA growth assessment. In Chapter 5, 3D volume growth assessment was more reliable than 2D size, with smaller interobsever differences, and more consistency across location. The smallest detectable change for 2D growth was larger than the current growth definition (1mm), leading to ambiguity in the current definition. 3D UIA quantitative morphology measures, such as flatness and shape index, were introduced in Chapter 6 and their relationship with UIA growth investigated. Even in non-growing (stable) aneurysms, morphology changed, suggesting that non-growing aneurysms could still be unstable. Quantified morphologic change should be considered for UIA growth and rupture risk assessment. Finally, in Chapter 7, a UIA growth prediction model using a vessel surface mesh convolutional neural network was developed. The model had comparable performance to patient demographic growth prediction models (ELAPSS). Mesh/morphology and patient models could be combined to provide a complete UIA growth prediction model. In conclusion, this thesis provides complete UIA characterisation from TOF-MRAs using computer-aided techniques. The automatic UIA detection and segmentation allows reliable, automatic UIA volume and morphology measurements. Such measurements aid in UIA growth assessment and formal UIA growth definitions including these measures should be investigated. As the accuracy of automatic UIA segmentation methods and growth prediction models increase, these will become more commonplace in clinical workflows. This could result in a fully automatic UIA characterisation tool, determining UIA volume, morphology and growth prediction scores to aid in treatment decision and improve patient outcome.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Image Analysis and Deep Learning Techniques for the Detection and Characterisation of Unruptured Intracranial Aneurysms

Timmins¹

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“…3D morphological measurements were performed as described elsewhere. 14 Briefly, we annotated aneurysms by drawing a contour around the aneurysm on each axial slice, using in-house developed software implemented in MeVisLab (MeVis Medical Solutions). The software subsequently fitted a mesh around the aneurysm using a marching cubes algorithm.…”

Section: Methodsmentioning

confidence: 99%

Three-Dimensional Morphological Change of Intracranial Aneurysms Before and Around Rupture

Kamphuis,

Timmins,

Kuijf

et al. 2024

Neurosurgery

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BACKGROUND AND OBJECTIVES: Patients with an unruptured intracranial aneurysm often undergo periodic imaging to detect potential aneurysm growth, which is associated with an increased rupture risk. Because prediction of rupture based on growth is moderate, morphological changes have gained interest as a risk factor for rupture. We studied 3-dimensional–quantified morphological changes over time during radiological monitoring before rupture and around rupture. METHODS: In this retrospective observational study, we identified aneurysms that ruptured during follow-up, with imaging available for at least 2 time points before rupture and one after rupture. For each time point, we obtained 8 morphological parameters: 2-dimensional size, volume, surface area, compactness 1 and 2, sphericity, elongation, and flatness. Morphological changes before rupture and around rupture were log-transformed, scaled, and analyzed with linear mixed-effects models. RESULTS: We included 16 aneurysms in 16 patients who were imaged between 2004 and 2021. In the time period before rupture (median follow-up duration 1200 days, IQR 736-1340), 3 size-related morphological parameters increased: 2-dimensional size (estimated mean change 0.44, 95% CI 0.24-0.65), volume (estimated mean change 0.34, 95% CI 0.12-0.56), and surface area (0.33, 95% CI 0.11-0.54). In the period around rupture (median follow-up duration 407 days, IQR 148-719), these parameters further increased. In addition, 5 morphological parameters (compactness 1 and 2, sphericity, elongation, and flatness) decreased around rupture but not before rupture. CONCLUSION: Change in aneurysm volume and surface area may be novel risk factors for rupture. Because most morphological parameters changed around but not before rupture, morphological changes during these 2 periods should be regarded as different processes. This implies that postrupture morphology should not be used as a surrogate for prerupture morphology in rupture prediction models.

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Future Unruptured Intracranial Aneurysm Growth Prediction Using Mesh Convolutional Neural Networks

Timmins

Kamphuis

Vos

et al. 2022

Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support

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Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Cited by 7 publications

References 22 publications

Image Analysis and Deep Learning Techniques for the Detection and Characterisation of Unruptured Intracranial Aneurysms

Image Analysis and Deep Learning Techniques for the Detection and Characterisation of Unruptured Intracranial Aneurysms

Three-Dimensional Morphological Change of Intracranial Aneurysms Before and Around Rupture

Future Unruptured Intracranial Aneurysm Growth Prediction Using Mesh Convolutional Neural Networks

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