## 4 Conclusions  
Fast propagation-based phase-contrast micro-X-ray computed tomography (\(\upmu\)XCT) is poised to become a valuable tool to study in situ dynamic processes at the cellular length scales in wood. To overcome the segmentation challenges associated with the decreased grayscale intensity contrast between object classes that occur when materials like wood are imaged with fast \(\upmu\)XCT, a modified U-net convolutional neural network (CNN) segmentation method was developed for segmenting wood \(\upmu\)XCT images into cell wall and air classes. The efficacy of the new segmentation method was demonstrated in experiments of loblolly pine wood imaged under different RH conditions. The accuracy of the CNN was quantified using the intersection over union (IoU) metric calculated through a comparison of ground-truth binary images to their corresponding CNN binary images. The CNN scored a mean IoU of 96%. Qualitative observations revealed that the small discrepancies between the ground-truth and CNN images primarily arose when segmenting pits, rays, and streak artifacts. Fortunately, these discrepancies represented a very small portion of the wood \(\upmu\)XCT images. A few specimens also displayed blurring noise that was likely caused by lateral vibrations during \(\upmu\)XCT imaging. The \(\upmu\)XCT images with blurring noise could not be reliably segmented and will need to be discarded in future quantitative analyses of this data set. Future \(\upmu\)XCT experiments should employ mechanical holders instead of mounting clay to hold specimens more securely and minimize lateral vibrations. Initial observations of moisture swelling in latewood cellular structure revealed that the cell walls swelled about 25% from 0% to 95% RH, and that in the transverse plane ray cell lumina became wider at higher RH. Overall, it was determined that the CNN was sufficient for future quantitative studies of cellular scale processes like moisture swelling in wood cellular structures. The CNN developed here would be expected to perform even better in synthetic cellular materials because synthetic materials lack the pits and rays that cause many of the uncertainties in the CNN segmented wood images. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13148146/s1](https://www.mdpi.com/article/10.3390/app13148146/s1)]([https://www.mdpi.com/article/10.3390/app13148146/s1](https://www.mdpi.com/article/10.3390/app13148146/s1)), Arzola_CNN_SM.pdf. Following are references that appear only in the Supplementary Materials: [45-57]. Conceptualization, X.A.-V., J.E.J., R.L. and D.S.S. ; methodology, X.A.-V., J.E.J., C.B., J.O., P.S., X.X. and F.D.C. ; formal analysis, X.A.-V., J.E.J. and C.B. ; data curation, X.A.-V. and J.E.J. ; writing--original draft preparation, X.A.-V.; writing--review and editing, J.E.J., C.B., R.L., D.S.S., J.O., P.S., X.X. and F.D.C. ; visualization, X.A.-V. and J.E.J. ; supervision, J.E.J., R.L. and D.S.S. ; funding acquisition, J.E.J., R.L. and D.S.S. All authors have read and agreed to the published version of the manuscript. This research used resources of the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science user facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06 CH11357. Support for X.A was provided by the Graduate Engineering Research Scholars (GERS) program at UW-Madison and USDA Forest Service grants 14-JV-11111129-063, 18-JV-11111129-036, and 22-JV-11111129-029. Data Availability Statement: Data can be available upon reasonable request to the corresponding author. We acknowledge the machine shop at the Forest Products Laboratory for construction of the in situ relative humidity chambers used in the \(\upmu\)XCT experiments. Thank you to [PERSON] of Shuqualak Lumber for sourcing the _Pinus taeda_. The authors declare no conflict of interest.