Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling

• URL: http://arxiv.org/abs/2207.14663v1
• Date: Fri, 29 Jul 2022 13:08:35 GMT
• Title: Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
• Authors: Dieuwertje Alblas, Christoph Brune, Kak Khee Yeung, Jelmer M. Wolterink
• Abstract summary: 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.
• Score: 3.435923468974656
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, or implicit representations such as radial basis functions or atomic (tubular) shapes. Here, we propose to represent surfaces by the zero level set of their signed distance function (SDF) 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. We here demonstrate the potential of this approach with three practical examples. First, we obtain an accurate and watertight surface for an abdominal aortic aneurysm (AAA) from CT images and show robust fitting from as little as 200 points on the surface. Second, we simultaneously fit nested vessel walls in a single INR without intersections. Third, we show how 3D models of individual arteries can be smoothly blended into a single watertight surface. Our results show that INRs are a flexible representation with potential for minimally interactive annotation and manipulation of complex vascular structures.

Related papers

• 3D Vessel Reconstruction from Sparse-View Dynamic DSA Images via Vessel Probability Guided Attenuation Learning [79.60829508459753]
  Current commercial Digital Subtraction Angiography (DSA) systems typically demand hundreds of scanning views to perform reconstruction. The dynamic blood flow and insufficient input of sparse-view DSA images present significant challenges to the 3D vessel reconstruction task. We propose to use a time-agnostic vessel probability field to solve this problem effectively.
  arXiv  Detail & Related papers  (2024-05-17T11:23:33Z)
• 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)
• 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)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• 3D Vessel Segmentation with Limited Guidance of 2D Structure-agnostic Vessel Annotations [3.6314292723682784]
  Supervised deep learning has demonstrated its superior capacity in automatic 3D vessel segmentation. The reliance on expensive 3D manual annotations and limited capacity for annotation reuse hinder the clinical applications of supervised models. This paper proposes a novel 3D shape-guided local discrimination model for 3D vascular segmentation under limited guidance from public 2D vessel annotations.
  arXiv  Detail & Related papers  (2023-02-07T07:26:00Z)
• 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)
• 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)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks [0.0]
  We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models. Key innovation introduced in the proposed approach is the ability to learn geometric uncertainty directly from training data.
  arXiv  Detail & Related papers  (2020-09-16T00:13:12Z)
• 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)

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.