Diffusion magnetic resonance imaging （dMRI）， as an advanced medical imaging technique， enables the reconstruction of white matter connectivity in the living brain at the macroscopic level. This technology provides an important tool for the quantitative description of brain structural connectivity and allows for quantitative analysis using connectivity or microstructural indices. Over the past two decades， the use of dMRI tractography to study brain connectivity has become a major direction in neuroimaging research. Tract segmentation is key to defining different quantitative regions in the analysis of brain connectivity. It enables the identification of white matter pathways that are meaningful for quantifying brain structural connections and supports quantitative comparisons of white matter pathways across subjects. This paper reviews tract segmentation methods and categorizes them into two major types based on their technical approaches： One type targets specific anatomical fiber bundles， focusing on tracts with clearly defined structures （such as the arcuate fasciculus and corticospinal tract）， and is suitable for task-oriented analysis and clinical navigation； the other type involves whole-brain tract segmentation methods， emphasizing data-driven or atlas-guided structural parcellation for the construction of large-scale structural connectivity networks and the implementation of whole-brain hierarchical analyses. In addition， this paper discusses the trade-offs of various methods in terms of applicability， accuracy， reproducibility， and computational cost. Although automated segmentation techniques have made significant progress in recent years， current methods still struggle to balance accuracy， generalizability and efficiency， and challenges remain in anatomical consistency， methodological standardization， and result interpretability. Data-driven deep learning methods have been rapidly developing in the field of tract segmentation， showing promising performance and holding potential for significant breakthroughs in the aforementioned areas.