Land plants have two types of shoot-supporting systems, root system and rhizoid system, in vascular plants and bryophytes. However, since the evolutionary origin of the systems are different, how much they exploit common systems or distinct systems to architect their structures are largely unknown. To understand the regulatory mechanism how bryophytes architect rhizoid system responding to environmental factors, we have developed the methodology to visualize and quantitatively analyze the rhizoid system of the moss, Physcomitrium patens in 3D. The rhizoids having the diameter of 21.3 µm on the average were visualized by refraction-contrast X-ray micro-CT using coherent X-ray optics available at synchrotron radiation facility SPring-8. Three types of shape (ring-shape, line, black circle) observed in tomographic slices of specimens embedded in paraffin were confirmed to be the rhizoids by optical and electron microscopy. Comprehensive automatic segmentation of the rhizoids which appeared in different three form types in tomograms was tested by a method using Canny edge detector or machine learning. Accuracy of output images was evaluated by comparing with the manually-segmented ground truth images using measures such as F1 score and IoU, revealing that the automatic segmentation using the machine learning was more effective than that using Canny edge detector. Thus, machine learning-based skeletonized 3D model revealed quite dense distribution of rhizoids. We successfully visualized the moss rhizoid system in 3D for the first time. High resolution refraction-contrast X-ray micro-CT using coherent X-ray optics successfully visualized 3D architecture of rhizoid system of moss, Physcomitrium patens, which is composed of cellular filaments having the diameter of 21.3 µm on the average, for the first time by using machine learning for segmentation.