摘要In plant science,understanding the hierarchical structure of leaf venations is crucial for insights into plant physiology,evolution,and ecology.However,data-driven segmentation methods are hampered by the lack of specialized datasets for hierarchical leaf vein analysis.To address this,we introduce the HierArchical Leaf Vein Segmentation(HALVS)dataset,the first of its kind,containing 5057 high-definition scanned leaf images from three species with 83.8 person-days of human annotations across three vein levels.We propose a novel label-efficient hierarchical segmentation framework combining Partially Supervised Semantic Segmentation(PSSS)and Denoising Diffusion Label Refinement(DDLR).PSSS classifies leaf pixels using primary and secondary vein annotations to generate high-confidence pseudo-labels for the background and tertiary veins,reducing omission errors.DDLR then refines these pseudo-labels,propagating structural priors from sparse veins to accurately recover tertiary veins.This framework significantly improves the integrity and connectivity of tertiary veins at low annotation costs.We also pioneer cross-species learning,training models on easily-annotated species and applying them to difficult ones.Despite challenges,DDLR remarkably enhances segmentation performance across all vein levels,providing an effective solution for complex hierarchical patterns and advancing agricul-tural research.