Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling

• URL: http://arxiv.org/abs/2403.11790v1
• Date: Mon, 18 Mar 2024 13:47:18 GMT
• Title: Deep Medial Voxels: Learned Medial Axis Approximations for Anatomical Shape Modeling
• Authors: Antonio Pepe, Richard Schussnig, Jianning Li, Christina Gsaxner, Dieter Schmalstieg, Jan Egger,
• Abstract summary: 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.
• Score: 5.584193645582203
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Shape reconstruction from imaging volumes is a recurring need in medical image analysis. Common workflows start with a segmentation step, followed by careful post-processing and,finally, ad hoc meshing algorithms. As this sequence can be timeconsuming, neural networks are trained to reconstruct shapes through template deformation. These networks deliver state-ofthe-art results without manual intervention, but, so far, they have primarily been evaluated on anatomical shapes with little topological variety between individuals. In contrast, other works favor learning implicit shape models, which have multiple benefits for meshing and visualization. Our work follows this direction by introducing deep medial voxels, a semi-implicit representation that faithfully approximates the topological skeleton from imaging volumes and eventually leads to shape reconstruction via convolution surfaces. Our reconstruction technique shows potential for both visualization and computer simulations.

Related papers

• Generative 3D Cardiac Shape Modelling for In-Silico Trials [0.0]
  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.
  arXiv  Detail & Related papers  (2024-09-24T12:59:18Z)
• Intraoperative Registration by Cross-Modal Inverse Neural Rendering [61.687068931599846]
  We present a novel approach for 3D/2D intraoperative registration during neurosurgery via cross-modal inverse neural rendering. Our approach separates implicit neural representation into two components, handling anatomical structure preoperatively and appearance intraoperatively. We tested our method on retrospective patients' data from clinical cases, showing that our method outperforms state-of-the-art while meeting current clinical standards for registration.
  arXiv  Detail & Related papers  (2024-09-18T13:40:59Z)
• Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images [1.4249943098958722]
  We present $textitUNet-DeformSA$ and $textitTransDeformer$: novel attention-based deep neural networks that reconstruct the geometry of the lumbar spine with high spatial accuracy and mesh correspondence across patients. Experiment results show that our networks generate artifact-free geometry outputs, and the variant of $textitTransDeformer$ can predict the errors of a reconstructed geometry.
  arXiv  Detail & Related papers  (2024-03-30T03:23:52Z)
• 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)
• Image segmentation with traveling waves in an exactly solvable recurrent neural network [71.74150501418039]
  We show that a recurrent neural network can effectively divide an image into groups according to a scene's structural characteristics. We present a precise description of the mechanism underlying object segmentation in this network. We then demonstrate a simple algorithm for object segmentation that generalizes across inputs ranging from simple geometric objects in grayscale images to natural images.
  arXiv  Detail & Related papers  (2023-11-28T16:46:44Z)
• Landmark-free Statistical Shape Modeling via Neural Flow Deformations [0.5897108307012394]
  We present FlowSSM, a novel shape modeling approach that learns shape variability without requiring dense correspondence between training instances. Our model outperforms state-of-the-art methods in providing an expressive and robust shape prior for distal femur and liver.
  arXiv  Detail & Related papers  (2022-09-14T18:17:19Z)
• Bayesian Inversion for Nonlinear Imaging Models using Deep Generative Priors [24.544313203472992]
  We develop a tractable posterior-sampling scheme based on the Metropolis-adjusted Langevin algorithm for the class of nonlinear inverse problems. We illustrate the advantages of our framework by applying it to two nonlinear imaging modalities-phase retrieval and optical diffraction tomography.
  arXiv  Detail & Related papers  (2022-03-18T17:47:29Z)
• Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
  Deep image prior was introduced as an effective prior for image reconstruction. Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques. We develop a two-stage learning paradigm to address the computational challenge.
  arXiv  Detail & Related papers  (2021-11-23T15:08:26Z)
• Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation [84.7571086566595]
  We propose improvements over previous GAN-based medical image synthesis methods by jointly encoding the intrinsic relationship of geometry and shape. The proposed method outperforms state-of-the-art segmentation methods on the public RETOUCH dataset having images captured from different acquisition procedures.
  arXiv  Detail & Related papers  (2020-03-31T11:50:43Z)
• Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering [53.16864661460889]
  Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. In this paper, we introduce body segmentation as critical supervision. To improve the reconstruction with part segmentation, we propose a part-level differentiable part that enables part-based models to be supervised by part segmentation.
  arXiv  Detail & Related papers  (2020-03-24T14:25:46Z)

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.