## 3 Results and Discussion
### Qualitative Observations  
Visual comparisons between reconstructed grayscale, ground-truth binary, and CNN binary images are shown in Figure 6. These close-up images were chosen to highlight a few qualitative observations in the segmented wood cellular structure. Softwood is primarily composed of longitudinal tracheids, whose cross-sections occupy most of the images. The cell walls and lumina in these tracheids are well-defined in the grayscale images and the CNN did well to segment them. Often, the cell wall-lumen interfaces are smoother in the CNN than in the in the ground-truth images, which is more consistent with the grayscale images and suggests the CNN segmentation can outperform manual segmentation. However, some differences between the ground-truth and CNN segmentations were observed in the pits and rays. The arrows in Figure 6a-c show cross sections of two pits that were missed in the manually segmented ground-truth image but were properly segmented by the CNN. The pit in the dotted circle in Figure 6a-c was not properly segmented in the ground-truth nor CNN images. The ray cells, which are indicated by dashed ellipses in Figure 6d-i, are less regularly defined than the longitudinal tracheids in the grayscale images. Even when manually segmenting the ground-truth images some user judgments had to be made with regard to which pixels corresponded to cell wall and air classes in the rays. Although differences were sometimes observed between the ground-truth and CNN segmentations, it was often not possible to unambiguously determine the correct pixel classification. Fortunately, the segmentation uncertainties in pits and rays are not expected to have a substantial effect on the cell wall quantification because most of softwood is composed of tracheids. In loblolly pine, tracheids occupy 96% of the total wood cellular volume and account for 99% of the total cell wall material [44]. Therefore, uncertainties in the segmenting of pits and rays would not be expected to be more than a few percent at most, which is consistent with the quantified CNN mean IoU of 96%. Streak artifacts caused by the image reconstruction algorithm were also observed to occasionally cause misclassifications in the CNN segmented images. The dashed ellipses in Figure 7a-d show streak artifacts in grayscale images that were partially segmented into the cell wall class in the CNN images. Figure 7e-d shows a similar streak artifact in a grayscale image that was not segmented into the cell wall class. These strong streak artifacts that were sometimes erroneously segmented into the cell wall class were only observed to appear at the edges of wood specimens with large ray cells, such as in the chosen close-ups in Figure 7. As is shown in the next section, streak artifacts that are segmented into the cell wall class affect the three-dimensional visualization of wood specimens. However, the number of pixels in the segmented streak artifacts is very small compared to the number of pixels in the cell wall material. For example, the percentages of pixels in Figure 7b,d that were misclassified in the streak artifact were 0.9% and 0.5%, respectively. Given Figure 7b,d depicts only close-up views of portions of the entire specimen, the actual percentage error would be much smaller and would not be expected to substantially affect cell wall volumes measured from the CNN segmented images. Figure 6: Comparison of (**a**,**d**,**g**) grayscale, (**b**,**e**,**h**) ground-truth binary, and (**c**,**f**,**i**) convolutional neural network (CNN) binary images. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values. Figure 7: Comparisons of (**a**,**c**,**e**) grayscale and (**b**,**d**,**f**) convolutional neural networks (CNN) binary images showing streak artifacts. The grayscale images are 8-bit with no filters applied post-reconstruction. Intensity bar has units of grayscale values.