Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario

• URL: http://arxiv.org/abs/2411.02477v1
• Date: Mon, 04 Nov 2024 18:08:24 GMT
• Title: Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario
• Authors: Rafic Nader, Florent Autrusseau, Vincent L'Allinec, Romain Bourcier,
• Abstract summary: This model intends to provide a dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, and we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.
• Score: 0.8749675983608172
• License:
• Abstract: We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial Aneurysms. The cerebral aneurysms most often occur on a particular structure of the vascular tree named the Circle of Willis. Various studies have been conducted to detect and monitor the aneurysms and those based on Deep Learning achieve the best performance. Specifically, in this work, we propose a full synthetic 3D model able to mimic the brain vasculature as acquired by Magnetic Resonance Angiography, Time Of Flight principle. Among the various MRI modalities, this latter allows for a good rendering of the blood vessels and is non-invasive. Our model has been designed to simultaneously mimic the arteries' geometry, the aneurysm shape, and the background noise. The vascular tree geometry is modeled thanks to an interpolation with 3D Spline functions, and the statistical properties of the background noise is collected from angiography acquisitions and reproduced within the model. In this work, we thoroughly describe the synthetic vasculature model, we build up a neural network designed for aneurysm segmentation and detection, finally, we carry out an in-depth evaluation of the performance gap gained thanks to the synthetic model data augmentation.

Related papers

• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• A vascular synthetic model for improved aneurysm segmentation and detection via Deep Neural Networks [0.8749675983608172]
  Our goal was to provide a dataset of brain arteries which could be used by a 3D Convolutional Neural Network (CNN) Our model has been designed to simultaneously mimic the arteries geometry, the ICA shape and the background noise.
  arXiv  Detail & Related papers  (2024-03-27T16:22:45Z)
• Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting [3.9347915104376168]
  Monitoring diseases that affect the brain's structural integrity requires automated analysis of magnetic resonance (MR) images. We modify state-of-the-art 2D, pseudo-3D, and 3D methods working in the image space, as well as 3D latent and 3D wavelet diffusion models, and train them to synthesize healthy brain tissue. Our evaluation shows that the pseudo-3D model performs best regarding the structural-similarity index, peak signal-to-noise ratio, and mean squared error.
  arXiv  Detail & Related papers  (2024-03-21T15:52:05Z)
• Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network [1.324564545341267]
  We present a novel approach for a robust semi-automatic vessel tracing algorithm on human fundus images by an instance segmentation neural network (InSegNN) InSegNN separates and labels different vascular trees individually and therefore enable tracing each tree throughout its branching. We have demonstrated tracing individual vessel trees from fundus images, and simultaneously retain the vessel hierarchy information.
  arXiv  Detail & Related papers  (2024-02-15T16:25:28Z)
• On the Localization of Ultrasound Image Slices within Point Distribution Models [84.27083443424408]
  Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US) Longitudinal tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. We present a framework for automated US image slice localization within a 3D shape representation.
  arXiv  Detail & Related papers  (2023-09-01T10:10:46Z)
• VesselVAE: Recursive Variational Autoencoders for 3D Blood Vessel Synthesis [0.879967413208593]
  We present a data-driven generative framework for synthesizing blood vessel 3D geometry. VesselVAE fully exploits the hierarchical organization of the vessel and learns a low-dimensional manifold. We generate 3D models of blood vessels that are both accurate and diverse, which is crucial for medical and surgical training, hemodynamic simulations, and many other purposes.
  arXiv  Detail & Related papers  (2023-07-07T13:35:48Z)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• Towards Autonomous Atlas-based Ultrasound Acquisitions in Presence of Articulated Motion [48.52403516006036]
  This paper proposes a vision-based approach allowing autonomous robotic US limb scanning. To this end, an atlas MRI template of a human arm with annotated vascular structures is used to generate trajectories. In all cases, the system can successfully acquire the planned vascular structure on volunteers' limbs.
  arXiv  Detail & Related papers  (2022-08-10T15:39:20Z)
• Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling [3.435923468974656]
  Personalised 3D vascular models are valuable for diagnosis, prognosis and treatment planning in patients with cardiovascular disease. Traditionally, such models have been constructed with explicit representations such as meshes and voxel masks. Here, we propose to represent surfaces by the zero level set of their signed distance function in a differentiable implicit neural representation (INR) This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms.
  arXiv  Detail & Related papers  (2022-07-29T13:08:35Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

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.