Details
| Original language | English |
|---|---|
| Article number | 108047 |
| Journal | Applied clay science |
| Volume | 279 |
| Early online date | 14 Nov 2025 |
| Publication status | Published - Jan 2026 |
Abstract
In this study, an automatic method for segmenting pores in scanning electron microscopy images was developed. An ensemble of machine learning classifiers was combined with a fully connected conditional random field to obtain a spatial pore probability field. This field was then thresholded to produce coherent binary pore masks, and a confidence per pore Cl was defined to quantify the reliability of the segmentation. The approach was demonstrated on a broad-ion-beam polished sample of the shaley facies of the Opalinus Clay. Accurate segmentation enabled the derivation of pore size distributions (PSD), pore morphologies, orientations, and spatial statistics. By using the median of Cl per size range, a data-driven lower truncation limit for PSD fitting was established. The resulting microstructural metrics supported the interpretation of rock properties such as permeability. These results highlighted the method’s relevance for materials such as Opalinus Clay, which is investigated as a potential candidate for a host rock for nuclear waste storage.
Keywords
- Machine learning, Opalinus Clay, Pore segmentation, Scanning Electron Microscopy, Uncertainty quantification
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
- Agricultural and Biological Sciences(all)
- Soil Science
- Earth and Planetary Sciences(all)
- Geology
- Earth and Planetary Sciences(all)
- Geochemistry and Petrology
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In: Applied clay science, Vol. 279, 108047, 01.2026.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Pore segmentation in electron micrographs
T2 - A probabilistic approach by ensemble machine learning
AU - Brysch, Marco
AU - Laurich, Ben
AU - Sester, Monika
N1 - Publisher Copyright: © 2025 The Authors.
PY - 2026/1
Y1 - 2026/1
N2 - In this study, an automatic method for segmenting pores in scanning electron microscopy images was developed. An ensemble of machine learning classifiers was combined with a fully connected conditional random field to obtain a spatial pore probability field. This field was then thresholded to produce coherent binary pore masks, and a confidence per pore Cl was defined to quantify the reliability of the segmentation. The approach was demonstrated on a broad-ion-beam polished sample of the shaley facies of the Opalinus Clay. Accurate segmentation enabled the derivation of pore size distributions (PSD), pore morphologies, orientations, and spatial statistics. By using the median of Cl per size range, a data-driven lower truncation limit for PSD fitting was established. The resulting microstructural metrics supported the interpretation of rock properties such as permeability. These results highlighted the method’s relevance for materials such as Opalinus Clay, which is investigated as a potential candidate for a host rock for nuclear waste storage.
AB - In this study, an automatic method for segmenting pores in scanning electron microscopy images was developed. An ensemble of machine learning classifiers was combined with a fully connected conditional random field to obtain a spatial pore probability field. This field was then thresholded to produce coherent binary pore masks, and a confidence per pore Cl was defined to quantify the reliability of the segmentation. The approach was demonstrated on a broad-ion-beam polished sample of the shaley facies of the Opalinus Clay. Accurate segmentation enabled the derivation of pore size distributions (PSD), pore morphologies, orientations, and spatial statistics. By using the median of Cl per size range, a data-driven lower truncation limit for PSD fitting was established. The resulting microstructural metrics supported the interpretation of rock properties such as permeability. These results highlighted the method’s relevance for materials such as Opalinus Clay, which is investigated as a potential candidate for a host rock for nuclear waste storage.
KW - Machine learning
KW - Opalinus Clay
KW - Pore segmentation
KW - Scanning Electron Microscopy
KW - Uncertainty quantification
UR - http://www.scopus.com/inward/record.url?scp=105021857811&partnerID=8YFLogxK
U2 - 10.1016/j.clay.2025.108047
DO - 10.1016/j.clay.2025.108047
M3 - Article
AN - SCOPUS:105021857811
VL - 279
JO - Applied clay science
JF - Applied clay science
SN - 0169-1317
M1 - 108047
ER -