Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning

• URL: http://arxiv.org/abs/2505.10407v1
• Date: Thu, 15 May 2025 15:30:41 GMT
• Title: Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning
• Authors: Wenhao Ding, Choon Hwai Yap, Kangjun Ji, Simão Castro,
• Abstract summary: A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time.<n>We propose AneuG, a two-stage Variational Autoencoder (VAE)-based IA mesh generator.
• Score: 5.424055089770432
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A generative model for the mesh geometry of intracranial aneurysms (IA) is crucial for training networks to predict blood flow forces in real time, which is a key factor affecting disease progression. This need is necessitated by the absence of a large IA image datasets. Existing shape generation methods struggle to capture realistic IA features and ignore the relationship between IA pouches and parent vessels, limiting physiological realism and their generation cannot be controlled to have specific morphological measurements. We propose AneuG, a two-stage Variational Autoencoder (VAE)-based IA mesh generator. In the first stage, AneuG generates low-dimensional Graph Harmonic Deformation (GHD) tokens to encode and reconstruct aneurysm pouch shapes, constrained to morphing energy statistics truths. GHD enables more accurate shape encoding than alternatives. In the second stage, AneuG generates parent vessels conditioned on GHD tokens, by generating vascular centreline and propagating the cross-section. AneuG's IA shape generation can further be conditioned to have specific clinically relevant morphological measurements. This is useful for studies to understand shape variations represented by clinical measurements, and for flow simulation studies to understand effects of specific clinical shape parameters on fluid dynamics. Source code and implementation details are available at https://github.com/anonymousaneug/AneuG.

Related papers

• Deformable registration and generative modelling of aortic anatomies by auto-decoders and neural ODEs [4.299467172990462]
  This work introduces AD-SVFD, a deep learning model for the registration of shapes to a pre-defined reference and for the generation of synthetic anatomies.<n>A distinctive feature of AD-SVFD is its auto-decoder structure, that enables generalization across shape cohorts and favors efficient weight sharing.<n>The use of implicit shape representations enables generative applications: new anatomies can be synthesized by suitably sampling from the latent space and applying the corresponding inverse transformations to the reference geometry.
  arXiv  Detail & Related papers  (2025-06-01T10:30:58Z)
• Aneumo: A Large-Scale Multimodal Aneurysm Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks [16.753219355754222]
  Intracranial aneurysms (IAs) are serious cerebrovascular lesions found in approximately 5% of the general population.<n>Their rupture may lead to high mortality.<n>Current methods for assessing IA risk focus on morphological and patient-specific factors, but the hemodynamic influences on IA development and rupture remain unclear.<n>This dataset aims to advance aneurysm research and promote data-driven approaches in biofluids, biomedical engineering, and clinical risk assessment.
  arXiv  Detail & Related papers  (2025-05-19T09:32:09Z)
• Domain Adaptive Diabetic Retinopathy Grading with Model Absence and Flowing Data [45.75724873443564]
  Domain shift poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy grading.<n>We propose a novel approach, Generative Unadversarial ExampleS (GUES), which enables adaptation from a data-centric perspective.
  arXiv  Detail & Related papers  (2024-12-02T07:14:25Z)
• Geometry-Aware Latent Representation Learning for Modeling Disease Progression of Barrett's Esophagus [0.0]
  Barrett's Esophagus (BE) is the only precursor known to Esophageal Adenocarcinoma (EAC) Unsupervised representation learning via Variational Autoencoders (VAEs) shows promise. VAEs map input data to a lower-dimensional manifold with only useful features. $mathcalS$-VAE outperforms vanilla VAE with better reconstruction losses, representation classification accuracies, and higher-quality generated images.
  arXiv  Detail & Related papers  (2023-03-17T14:08:16Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection [52.93342510469636]
  Lung nodule detection in chest X-ray (CXR) images is common to early screening of lung cancers. Deep-learning-based Computer-Assisted Diagnosis (CAD) systems can support radiologists for nodule screening in CXR. To alleviate the limited availability of such datasets, lung nodule synthesis methods are proposed for the sake of data augmentation.
  arXiv  Detail & Related papers  (2022-07-19T16:38:48Z)
• CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis [3.7814216736076434]
  CASHformer is a transformer-based framework to model longitudinal shape trajectories in Alzheimer's disease. It reduces the number of parameters by over 90% with respect to the original model. Our results show that CASHformer reduces the reconstruction error by 73% compared to previously proposed methods.
  arXiv  Detail & Related papers  (2022-07-05T14:50:21Z)
• Modeling and hexahedral meshing of cerebral arterial networks from centerlines [0.0]
  Centerline-based representation is widely used to model large vascular networks with small vessels. We propose an automatic method to generate a structured hexahedral mesh suitable for CFD directly from centerlines. We demonstrate the efficiency of our method by entirely meshing a dataset of 60 cerebral vascular networks.
  arXiv  Detail & Related papers  (2022-01-20T16:30:17Z)
• Modeling and Predicting Blood Flow Characteristics through Double Stenosed Artery from CFD simulation using Deep Learning Models [0.0]
  We train deep learning models to learn and predict blood flow characteristics using a dataset generated by CFD simulations of simplified double stenosed artery models. A novel geometric representation of the constricted neck is proposed which, in terms of a generalized simplified model, outperforms the former assumption.
  arXiv  Detail & Related papers  (2021-12-04T11:16:28Z)
• SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection [76.01333073259677]
  We propose the use of Space-aware Memory Queues for In-painting and Detecting anomalies from radiography images (abbreviated as SQUID) We show that SQUID can taxonomize the ingrained anatomical structures into recurrent patterns; and in the inference, it can identify anomalies (unseen/modified patterns) in the image.
  arXiv  Detail & Related papers  (2021-11-26T13:47:34Z)
• Data-driven generation of plausible tissue geometries for realistic photoacoustic image synthesis [53.65837038435433]
  Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties. We propose a novel approach to PAT data simulation, which we refer to as "learning to simulate" We leverage the concept of Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data to generate plausible tissue geometries.
  arXiv  Detail & Related papers  (2021-03-29T11:30:18Z)
• Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification [48.4619620405991]
  Mammogram benign or malignant classification with only image-level labels is challenging due to the absence of lesion annotations. Motivated by the symmetric prior, we can explore a counterfactual problem that how would the features have behaved if there were no lesions in the image. We derive a new theoretical result for counterfactual generation based on the symmetric prior.
  arXiv  Detail & Related papers  (2020-09-30T03:15:30Z)
• Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [102.02672176520382]
  Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has, but can only infer the temporal relationship between each co-morbid condition. We develop deep diffusion processes to model "dynamic comorbidity networks"
  arXiv  Detail & Related papers  (2020-01-08T15:47:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.