Generative 3D Cardiac Shape Modelling for In-Silico Trials

• URL: http://arxiv.org/abs/2409.16058v1
• Date: Tue, 24 Sep 2024 12:59:18 GMT
• Title: Generative 3D Cardiac Shape Modelling for In-Silico Trials
• Authors: Andrei Gasparovici, Alex Serban,
• Abstract summary: We propose a deep learning method to model and generate synthetic aortic shapes. The network is trained on a dataset of aortic root meshes reconstructed from CT images. By sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a deep learning method to model and generate synthetic aortic shapes based on representing shapes as the zero-level set of a neural signed distance field, conditioned by a family of trainable embedding vectors with encode the geometric features of each shape. The network is trained on a dataset of aortic root meshes reconstructed from CT images by making the neural field vanish on sampled surface points and enforcing its spatial gradient to have unit norm. Empirical results show that our model can represent aortic shapes with high fidelity. Moreover, by sampling from the learned embedding vectors, we can generate novel shapes that resemble real patient anatomies, which can be used for in-silico trials.

Related papers

• Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling [5.584193645582203]
  We introduce deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes. Our reconstruction technique shows potential for both visualization and computer simulations.
  arXiv  Detail & Related papers  (2024-03-18T13:47:18Z)
• An End-to-End Deep Learning Generative Framework for Refinable Shape Matching and Generation [45.820901263103806]
  Generative modelling for shapes is a prerequisite for In-Silico Clinical Trials (ISCTs) We develop a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space. We extend our proposed base model to a joint shape generative-clustering multi-atlas framework to incorporate further variability.
  arXiv  Detail & Related papers  (2024-03-10T21:33:53Z)
• GEM3D: GEnerative Medial Abstractions for 3D Shape Synthesis [25.594334301684903]
  We introduce GEM3D -- a new deep, topology-aware generative model of 3D shapes. Key ingredient of our method is a neural skeleton-based representation encoding information on both shape topology and geometry. We demonstrate significantly more faithful surface reconstruction and diverse shape generation results compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2024-02-26T20:00:57Z)
• ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction [59.971808117043366]
  ReShapeIT represents an anatomical structure with an implicit template field shared within the same category. It ensures the implicit template field generates valid templates by strengthening the constraint of the correspondence between the instance shape and the template shape. A template Interaction Module is introduced to reconstruct unseen shapes by interacting the valid template shapes with the instance-wise latent codes.
  arXiv  Detail & Related papers  (2023-12-11T07:09:32Z)
• TailorMe: Self-Supervised Learning of an Anatomically Constrained Volumetric Human Shape Model [4.474107938692397]
  Human shape spaces have been extensively studied, as they are a core element of human shape and pose inference tasks. We create an anatomical template, consisting of skeleton bones and soft tissue, to the surface scans of the CAESAR database. This data is then used to learn an anthropo constrained volumetric human shape model in a self-supervised fashion.
  arXiv  Detail & Related papers  (2023-11-03T07:42:19Z)
• Neural Deformable Models for 3D Bi-Ventricular Heart Shape Reconstruction and Modeling from 2D Sparse Cardiac Magnetic Resonance Imaging [8.12099697240624]
  We propose a novel neural deformable model (NDM) targeting at the reconstruction and modeling of 3D bi-ventricular shape of the heart. We model the bi-ventricular shape using blended deformable superquadrics, which are parameterized by a set of geometric parameter functions. Our NDM can learn to densify a sparse cardiac point cloud with arbitrary scales and generate high-quality triangular meshes automatically.
  arXiv  Detail & Related papers  (2023-07-15T03:11:16Z)
• Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation [54.09274684734721]
  We present a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets. We may jointly train an encoder network to learn a latent space for inverting shapes, allowing us to enable a rich variety of whole-shape and region-aware shape manipulations.
  arXiv  Detail & Related papers  (2023-02-01T02:47:53Z)
• A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras [52.33206865588584]
  We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. We build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity.
  arXiv  Detail & Related papers  (2022-10-04T13:36:52Z)
• Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology-Varying Objects [52.46838926521572]
  3D-aware generative models have demonstrated their superb performance to generate 3D neural radiance fields (NeRF) from a collection of monocular 2D images. We propose a generative model for synthesizing radiance fields of topology-varying objects with disentangled shape and appearance variations.
  arXiv  Detail & Related papers  (2022-09-09T08:44:06Z)
• Neural Template: Topology-aware Reconstruction and Disentangled Generation of 3D Meshes [52.038346313823524]
  This paper introduces a novel framework called DTNet for 3D mesh reconstruction and generation via Disentangled Topology. Our method is able to produce high-quality meshes, particularly with diverse topologies, as compared with the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-06-10T08:32:57Z)

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.