Segmentation of 3D pore space from CT images using curvilinear skeleton:
application to numerical simulation of microbial decomposition
- URL: http://arxiv.org/abs/2309.01611v1
- Date: Mon, 4 Sep 2023 13:51:40 GMT
- Title: Segmentation of 3D pore space from CT images using curvilinear skeleton:
application to numerical simulation of microbial decomposition
- Authors: Olivier Monga and Zakaria Belghali and Mouad Klai and Lucie Druoton
and Dominique Michelucci and Valerie Pot
- Abstract summary: Voxel-based description (up to hundreds millions voxels) of the pore space can be extracted, from grey level 3D CT scanner images, by means of simple image processing tools.
Several recent works propose basic analytic volume primitives to define a piece-wise approximation of pore space for numerical simulation of draining, diffusion and microbial decomposition.
Here, we study another alternative where pore space is described by means of geometrically relevant connected subsets of voxels (regions) computed from the curvilinear skeleton.
- Score: 1.0485739694839669
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advances in 3D X-ray Computed Tomographic (CT) sensors have stimulated
research efforts to unveil the extremely complex micro-scale processes that
control the activity of soil microorganisms. Voxel-based description (up to
hundreds millions voxels) of the pore space can be extracted, from grey level
3D CT scanner images, by means of simple image processing tools. Classical
methods for numerical simulation of biological dynamics using mesh of voxels,
such as Lattice Boltzmann Model (LBM), are too much time consuming. Thus, the
use of more compact and reliable geometrical representations of pore space can
drastically decrease the computational cost of the simulations. Several recent
works propose basic analytic volume primitives (e.g. spheres, generalized
cylinders, ellipsoids) to define a piece-wise approximation of pore space for
numerical simulation of draining, diffusion and microbial decomposition. Such
approaches work well but the drawback is that it generates approximation
errors. In the present work, we study another alternative where pore space is
described by means of geometrically relevant connected subsets of voxels
(regions) computed from the curvilinear skeleton. Indeed, many works use the
curvilinear skeleton (3D medial axis) for analyzing and partitioning 3D shapes
within various domains (medicine, material sciences, petroleum engineering,
etc.) but only a few ones in soil sciences. Within the context of soil
sciences, most studies dealing with 3D medial axis focus on the determination
of pore throats. Here, we segment pore space using curvilinear skeleton in
order to achieve numerical simulation of microbial decomposition (including
diffusion processes). We validate simulation outputs by comparison with other
methods using different pore space geometrical representations (balls, voxels).
Related papers
- Proper Latent Decomposition [4.266376725904727]
We compute a reduced set of intrinsic coordinates (latent space) to accurately describe a flow with fewer degrees of freedom than the numerical discretization.
With this proposed numerical framework, we propose an algorithm to perform PLD on the manifold.
This work opens opportunities for analyzing autoencoders and latent spaces, nonlinear reduced-order modeling and scientific insights into the structure of high-dimensional data.
arXiv Detail & Related papers (2024-12-01T12:19:08Z) - μ-Net: A Deep Learning-Based Architecture for μ-CT Segmentation [2.012378666405002]
X-ray computed microtomography (mu-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples.
extracting relevant information from 3D images requires semantic segmentation of the regions of interest.
We propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus.
arXiv Detail & Related papers (2024-06-24T15:29:08Z) - A Voxel-based Approach for Simulating Microbial Decomposition in Soil: Comparison with LBM and Improvement of Morphological Models [0.0]
This study presents a new computational approach for simulating the microbial decomposition of organic matter.
The method employs a valuated graph of connected voxels to simulate transformation and diffusion processes.
The resulting model can be adapted to simulate any diffusion-transformation processes in porous media.
arXiv Detail & Related papers (2024-06-06T15:35:25Z) - Topological reconstruction of sampled surfaces via Morse theory [4.166095721909433]
We present a reconstruction algorithm based on a careful topological study of the point sample.
No triangulation or local implicit equations are used as intermediate steps.
The algorithm can be applied to smooth surfaces with or without boundary, embedded in an ambient space of any dimension.
arXiv Detail & Related papers (2024-05-27T15:14:47Z) - Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations.
We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
arXiv Detail & Related papers (2023-09-01T16:59:21Z) - 3D Molecular Geometry Analysis with 2D Graphs [79.47097907673877]
Ground-state 3D geometries of molecules are essential for many molecular analysis tasks.
Modern quantum mechanical methods can compute accurate 3D geometries but are computationally prohibitive.
We propose a novel deep learning framework to predict 3D geometries from molecular graphs.
arXiv Detail & Related papers (2023-05-01T19:00:46Z) - Deep learning of multi-resolution X-Ray micro-CT images for multi-scale
modelling [0.0]
We develop a 3D Enhanced Deep Super Resolution (EDSR) convolutional neural network to create enhanced, high-resolution data over large spatial scales.
We validate the network with various metrics: textual analysis, segmentation behaviour and pore-network model (PNM) multiphase flow simulations.
The EDSR generated model is more accurate than the base LR model at predicting experimental behaviour in the presence of heterogeneities.
arXiv Detail & Related papers (2021-11-01T21:49:22Z) - Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular
Graphs [79.06686274377009]
We develop a benchmark, known as Molecule3D, that includes a dataset with precise ground-state geometries of approximately 4 million molecules.
We implement two baseline methods that either predict the pairwise distance between atoms or atom coordinates in 3D space.
Our method can achieve comparable prediction accuracy but with much smaller computational costs.
arXiv Detail & Related papers (2021-09-30T22:09:28Z) - Volume Rendering of Neural Implicit Surfaces [57.802056954935495]
This paper aims to improve geometry representation and reconstruction in neural volume rendering.
We achieve that by modeling the volume density as a function of the geometry.
Applying this new density representation to challenging scene multiview datasets produced high quality geometry reconstructions.
arXiv Detail & Related papers (2021-06-22T20:23:16Z) - GeoMol: Torsional Geometric Generation of Molecular 3D Conformer
Ensembles [60.12186997181117]
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Existing generative models have several drawbacks including lack of modeling important molecular geometry elements.
We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
arXiv Detail & Related papers (2021-06-08T14:17:59Z) - Multi-Scale Neural Networks for to Fluid Flow in 3D Porous Media [0.0]
We develop a general multiscale deep learning model that is able to learn from porous media simulation data.
We enable the evaluation of large images in approximately one second on a single Graphics Processing Unit.
arXiv Detail & Related papers (2021-02-10T23:38:36Z) - Disentangling and Unifying Graph Convolutions for Skeleton-Based Action
Recognition [79.33539539956186]
We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D.
By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
arXiv Detail & Related papers (2020-03-31T11:28:25Z)
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.