Direct imaging methods, among which X-ray computed tomography (XCT) continues to dominate, enable the study of soil structure at different scales. However, to compute different morphological parameters or assess soil physical properties using pore-scale modelling we need to perform image segmentation to divide the XCT greyscale image representing local absorption of X-ray radiation into major constituents or phases. Here we focused on the simplest type of segmentation procedure – binarization into pores and solid phases. We present the initial results for soil XCT image segmentation using convolutional neural networks (CNN). We assumed that current state-of-the-art local segmentation approaches could provide ground truth data to perform neural network training. We used hybrid U-net + ResNet-101 architecture and segmented seven soil XCT images. The training was performed by excluding the segmented image from training and validation datasets. The segmentations’ accuracy was assessed using standard computer vision metrics (precision, recall, intersection over union or IoU) and pore-scale simulations to compute the permeability of resulting 3D binary soil images. Depending on the soil sample, the error of segmentations in terms of computed hydraulic properties varied from 5% to 130%. The IoU metric was found to be the most sensitive to false positive and false negative porosity predictions by the neural network. To explain observed variations, we performed ground-truth and original XCT greyscale images analysis with the help of correlation and covariance functions. In addition to a comparison between images, we also trained another segmentation neural network that used all samples as a training/verification dataset that helped to explain the inaccuracies caused by insufficient representativeness of some soil sample structures in the training dataset. We discussed possible ways to improve the segmentation results in the future, including the usage of larger soil image libraries, physically modelled ground-truth data, and advanced neural network architectures.

在X射线计算机断层扫描（XCT）继续占主导地位的直接成像方法中，可以研究不同规模的土壤结构。但是，为了使用孔尺度模型计算不同的形态参数或评估土壤物理性质，我们需要执行图像分割，将代表局部吸收X射线辐射的XCT灰度图像划分为主要成分或相。在这里，我们集中于最简单的分割程序类型-二进制化为孔隙和固相。我们提出了使用卷积神经网络（CNN）进行土壤XCT图像分割的初步结果。我们假设当前最先进的局部分割方法可以提供地面真实数据来执行神经网络训练。我们使用了混合的U-net + ResNet-101架构并分割了七个土壤XCT图像。通过从训练和验证数据集中排除分割的图像来执行训练。使用标准的计算机视觉指标（精度，召回率，联合或IoU交集）和孔尺度模拟来评估分割的准确性，以计算所得3D二元土壤图像的渗透率。根据土壤样品的不同，分段的计算水力特性误差在5％到130％之间变化。通过神经网络发现，IoU度量对假阳性和假阴性孔隙度预测最敏感。为了解释观察到的变化，我们在相关和协方差函数的帮助下执行了地面真实性和原始XCT灰度图像分析。除了图像之间的比较之外，我们还训练了另一个分割神经网络，该网络使用所有样本作为训练/验证数据集，这有助于解释训练数据集中某些土壤样本结构的代表性不足引起的不准确性。我们讨论了将来改善分割结果的可能方法，包括使用更大的土壤图像库，物理建模的地面真实数据和先进的神经网络体系结构。