## 3 Results and Discussion
### Visualization  
Figure 9 shows three-dimensional renderings of a portion of a 1 mm cross-section latewood sample. Like the two-dimensional images, different wood anatomical features can be observed, such as rays and longitudinal tracheids on the exposed radial-longitudinal surface. The small features protruding from the exposed tangential-longitudinal face are streak artifacts and not a real anatomical feature of wood. With stacks of segmented uXCT images, it also possible to virtually separate different components to obtain information about the interior of the sample. For example, Figure 9b-d shows the longitudinal tracheid cell walls, longitudinal tracheid lumina, and ray cell lumina, respectively, from a sub-volume virtually excised from the specimen. In addition to visualization, virtual separations could also be used for quantitative analyses of the different components.  
To demonstrate moisture swelling, Figure 10 shows superimposed cross-sectional images at 0% and 95% RH. To better visualize the interior of the sample, the 0% RH image was processed to extract the sample edges. It can be readily observed that the size of the sample cross-section increased as the RH increased. Moreover, it can be observed in the interior of the sample that the width of the rays also increased. Swelling of the cell wall itself is not as easy to observe visually. Fortunately, the cell wall swelling can be easily quantified by using the known 0.65 um pixel size and summing the number of pixels in the cell wall class at each RH. For this cross-section, the cell wall area increased from 0.49 mm\({}^{2}\) to 0.61 mm\({}^{2}\) going from 0% to 95% RH. This is an approximately 25% increase. The increase in cell wall swelling is much larger than the estimated errors in the CNN segmentation results, which included from the calculated mean IoU an estimated misclassification of 4% of pixels and qualitative observations of small errors in the CNN segmentation of pits, rays, and streak artifacts. Therefore, CNN segmentation will be adequate to calculate cell wall swelling in wood over this range of RH because the expected amount of swelling is much  
Figure 8: Comparison of (**a**)grayscale and (**b**) convolutional neural network (CNN) binary images showing the effects of blurring noise in a 0.5 mm cross-section latewood specimen. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values.  
larger compared to the estimated uncertainties. The 25% increase in cell wall area from the results in Figure 10 was also consistent with previous work. [PERSON] and coworkers also used \(\upmu\)XCT to study swelling in latewood Norway spruce cell walls and reported a 12.5% increase in cell wall volume when measured over the smaller RH range of 10% to 85% [13].  
Figure 10: Superimposed 1 mm cross-sections of a latewood sample conditioned at 0% and 95% relative humidity.  
Figure 9: Three-dimensional renderings from a 1 mm cross-section latewood specimen conditioned at 0% relative humidity. The full image (**a**) is from 1400 image slices corresponding to a total length of 910 \(\upmu\)m. The sub volumes show virtually separated (**b**) longitudinal tracheid cell walls, (**c**) longitudinal tracheid lumina, and (**d**) ray cell lumina.