Vascular Surface Segmentation for Intracranial Aneurysm Isolation and Quantification

Bizjak, Žiga; Likar, Boštjan; Pernuš, Franjo; Špiclin, Žiga

doi:10.1007/978-3-030-59725-2_13

Cited by 11 publications

(7 citation statements)

References 24 publications

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“…However, the quality of their results was poor (46% in Dice similarity coefficient). Importantly, Yang et al [7] and Bizjak et al [8] made useful attempts to apply point-based networks to segment IAs. However, segmentation was only performed for surface fragments that were manually labeled as containing aneurysms.…”

Section: Segmentationmentioning

confidence: 99%

“…This approach is unrealistic in clinical practice. In addition, Bizjak et al [8] employed only the sensitivity of the entire input as an evaluation metric.…”

Section: Segmentationmentioning

confidence: 99%

“…Meanwhile, deep learning techniques are becoming increasingly popular in medical image processing; however, they are mostly used for classification and detection. Few prior Park et al [5] Sichterman et al [6] Yang et al [7] Bizjak et al [8] Our method research works have explored the application of deep learning methods to the segmentation of IAs, and their performance remains limited [6] (see Table 1 and Section 2). This study builds on Yang et al's work the IntrA dataset [7], which was created for surface-based classification and segmentation of IAs, and reported the performance of existing neural network models on both tasks.…”

Section: Introductionmentioning

confidence: 99%

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A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

et al. 2022

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The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this segmentation task, their performance remains limited. In this study, we offer a two-step surface-based deep learning pipeline that achieves significantly better results. Our proposed model takes a surface model of an entire set of principal brain arteries containing aneurysms as input and returns aneurysm surfaces as output. A user first generates a surface model by manually specifying multiple thresholds for time-of-flight magnetic resonance angiography images. The system then samples small surface fragments from the entire set of brain arteries and classifies the surface fragments according to whether aneurysms are present using a point-based deep learning network (PointNet++). Finally, the system applies surface segmentation (SO-Net) to surface fragments containing aneurysms. We conduct a direct comparison of the segmentation performance of our proposed surface-based framework and an existing voxel-based method by counting voxels: our framework achieves a much higher Dice similarity (72%) than the prior approach (46%).

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Section: Segmentationmentioning

confidence: 99%

“…This approach is unrealistic in clinical practice. In addition, Bizjak et al [8] employed only the sensitivity of the entire input as an evaluation metric.…”

Section: Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

et al. 2022

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“…Geometric deep learning models using meshes include MeshNet [22] and MeshCNN [23]. Studies have shown that geometric deep learning performs well for UIA segmentation when considering a smaller region-of-interest around an already detected UIA with its parent vessels [120][121][122].…”

Section: Geometric Deep-learning Methodsmentioning

confidence: 99%

“…Mesh neural networks have been used successfully in a few previous medical applications for classification [128] and segmentation [120,125], and we demonstrated that modality-independent UIA detection using mesh convolutional neural networks was possible with a small dataset [130]. As mesh convolutional neural networks continue to be developed, there is little information on optimal hyperparameter and configuration of mesh convolutional neural networks such as the resolution of input meshes, pooling layers and additional input features.…”

Section: Aimmentioning

confidence: 99%

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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Deformable Convolution and Semi-supervised Learning in Point Clouds for Aneurysm Classification and Segmentation

Meijering

Xia

et al. 2021

Communications in Computer and Information Science

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Vascular Surface Segmentation for Intracranial Aneurysm Isolation and Quantification

Cited by 11 publications

References 24 publications

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

A two-step surface-based 3D deep learning pipeline for segmentation of intracranial aneurysms

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

Deformable Convolution and Semi-supervised Learning in Point Clouds for Aneurysm Classification and Segmentation

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