Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures
Abstract
To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed tomore »
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1989717
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- Applied Sciences
- Additional Journal Information:
- Journal Name: Applied Sciences Journal Volume: 13 Journal Issue: 14; Journal ID: ISSN 2076-3417
- Publisher:
- MDPI AG
- Country of Publication:
- Switzerland
- Language:
- English
Citation Formats
Arzola-Villegas, Xavier, Báez, Carlos, Lakes, Roderic, Stone, Donald S., O’Dell, Jane, Shevchenko, Pavel, Xiao, Xianghui, De Carlo, Francesco, and Jakes, Joseph E. Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures. Switzerland: N. p., 2023.
Web. doi:10.3390/app13148146.
Arzola-Villegas, Xavier, Báez, Carlos, Lakes, Roderic, Stone, Donald S., O’Dell, Jane, Shevchenko, Pavel, Xiao, Xianghui, De Carlo, Francesco, & Jakes, Joseph E. Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures. Switzerland. https://doi.org/10.3390/app13148146
Arzola-Villegas, Xavier, Báez, Carlos, Lakes, Roderic, Stone, Donald S., O’Dell, Jane, Shevchenko, Pavel, Xiao, Xianghui, De Carlo, Francesco, and Jakes, Joseph E. Thu .
"Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures". Switzerland. https://doi.org/10.3390/app13148146.
@article{osti_1989717,
title = {Convolutional Neural Network for Segmenting Micro-X-ray Computed Tomography Images of Wood Cellular Structures},
author = {Arzola-Villegas, Xavier and Báez, Carlos and Lakes, Roderic and Stone, Donald S. and O’Dell, Jane and Shevchenko, Pavel and Xiao, Xianghui and De Carlo, Francesco and Jakes, Joseph E.},
abstractNote = {To further enhance the performance of wood products, improved tools are needed to study in situ cellular scale phenomena like mechanical deformations and moisture swelling. Micro-X-ray computed tomography (μXCT) using brilliant synchrotron light sources now has the spatial and temporal resolution for real-time visualization of phenomena in three-dimensional cellular structures. However, the tradeoff for speed includes the loss of intensity contrast between different types of materials within the imaged structure, such as cell wall and air in wood. This loss of contrast prevents traditional histogram-based segmentation methods from being used effectively. A new convolutional neural network (CNN) approach was therefore developed to segment fast μXCT images of wood into cell wall and air volumes. The fast μXCT and segmentation were demonstrated in the study of moisture swelling in loblolly pine (Pinus taeda) earlywood and latewood cellular structures conditioned at 0%, 33%, 75%, and 95% relative humidity (RH). The CNN segmentation results had a mean intersection over union (IoU) metric accuracy of 96%. Initial analysis of the swelling in the latewood revealed cell walls swelled about 25% when conditioned from 0% to 95% RH. Additionally, the widths of ray cell lumina in the transverse plane of latewood could be observed to increase at higher RH. The segmentation method presented here will facilitate future quantitative analyses in in situ μXCT studies of wood and other similar cellular materials.},
doi = {10.3390/app13148146},
journal = {Applied Sciences},
number = 14,
volume = 13,
place = {Switzerland},
year = {Thu Jul 13 00:00:00 EDT 2023},
month = {Thu Jul 13 00:00:00 EDT 2023}
}
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