Segmentation of 3D pore space from CT images using curvilinear skeleton: application to numerical simulation of microbial decomposition
Olivier Monga, Zakaria Belghali, Mouad Klai, Lucie Druoton, and Dominique Michelucci, Valerie Pot

TL;DR
This paper introduces a novel method for segmenting 3D pore space in soil CT images using curvilinear skeletons, enabling more accurate and efficient simulations of microbial decomposition processes.
Contribution
It presents a new skeleton-based segmentation approach for pore space, improving accuracy over primitive shape approximations in microbial decomposition simulations.
Findings
Skeleton-based segmentation improves simulation accuracy.
Method reduces computational costs compared to voxel-based models.
Validation shows better representation of pore connectivity.
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…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Cell Image Analysis Techniques · Lattice Boltzmann Simulation Studies
MethodsFocus · Diffusion
