Before high-speed \(\mu\)XCT imaging can be effectively utilized to study wood, improved segmentation methods are needed because the imaging parameters used to gain high temporal resolution in high-speed \(\mu\)XCT, such as employing higher energy (>10 keV) polychromatic X-ray beams and propagation-based imaging, result in \(\mu\)XCT volume reconstructions with less differential contrast between materials. The lower contrast prevents traditional histogram thresholding techniques from being used. Propagation-based imaging is a type of phase contrast imaging in which Fresnel diffraction at interfaces between materials with different refractive indices, such as wood cell walls and air in wood, results in interference fringes recorded by the detector in the projection images [23]. Increases in sample to detector distance increase the differences between the peaks and valleys in the recorded interference fringes. Therefore, increasing the sample to detector distance to incorporate environmental chambers or mechanical testing fixtures further enhances interference fringes in the phase contrast images. In reconstructed transverse wood cross-sections, these phase contrast effects result in large grayscale intensity variations near the lumen-cell wall interfaces that further inhibit successful implementation of traditional histogram-based thresholding segmentation techniques [24]. Fortunately, machine learning segmentation approaches based on convolutional neural networks (CNN) show promise for segmenting X-ray computed tomography images with limited intensity variations between material classes [25]. U-Net is a CNN that was originally developed for fast and precise segmentation of biomedical images [26]. Given U-Net's effectiveness for segmenting the cellular structure of HeLa cells in light microscopy images, U-net has promise for segmenting \(\mu\)XCT images of wood cellular structure. U-Net converts the image segmentation process into a pixel classification problem in which each specific pixel within an image is assigned to categories corresponding to different object classes [27]. To accomplish the pixel classification, the network is trained to learn from prepared reference images known as ground-truth images [28; 29]. The two main steps in the training process algorithm are forward propagation and back propagation [26]. Initially, filter weights are randomly assigned. In forward propagation, the algorithm performs a first prediction of segmentation using these random weights. The prediction is compared with the ground-truth image and an error is calculated using an error minimization function. This error is then backpropagated through the network to adjust the weights for a better prediction in the next iteration. The process is continued until the error is completely minimized or reaches a predefined minimization value [27; 28; 29]. Essentially, this means that the network will learn filters that capture visual information [28]. The filters are tuned to capture features from the training images such as edges, orientations, and ultimately entire patterns. After being trained, U-Net takes as input a single-channel gray-scale image and outputs a multi-channel image where each channel contains the probability information of each pixel belonging to a particular class [26]. In the case here for wood cellular structure, there will be two classes consisting of cell wall material and air. In this study, we employed a custom-built in situ \(\upmu\)XCT relative humidity (RH) chamber designed to study cellular-scale swelling processes in wood and other hygroscopic materials. The RH chamber was used with fast propagation-based phase-contrast \(\upmu\)XCT imaging to capture full tomogram data sets of wood in only 7.5 s. The goal was to develop \(\upmu\)XCT protocols to meet the recognized need for new tools to study dynamic wood-water interactions at the cellular length scale [9]. However, the resulting \(\upmu\)XCT volumes lacked sufficient contrast between the air and cell wall volumes.