用半监督方法提升高粱气孔组件的精准分割，助力抗旱育种研究。
StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components
- 将高清图像切块并结合伪标签策略，解决小结构分割难题。
- 实例分割准确率从28.30%提升至46.10%，显著优于传统方法。
- 适合作物表型分析与人工智能辅助育种研究者使用。
高粱是全球重要的粮食作物，广泛种植于干旱和逆境区域，其强抗旱性使其成为气候韧性农业的重点作物。提高高粱水分利用效率需精确解析气孔特征，因气孔调控气体交换、蒸腾和光合作用，对作物表现影响重大。但自动化分析困难，因气孔尺寸小（草类中常小于40 μm），且在不同基因型和叶面形态间形状差异大。现有方法面临嵌套微小结构识别与标注瓶颈。本文提出针对高粱气孔组分的半监督实例分割框架。构建含11,060张人工标注图像块的数据集，涵盖孔隙、保卫细胞和复合区域三种成分，覆盖多个基因型与叶面。为增强微小结构检测，将高分辨率显微图像切分为重叠小块，并对未标注图像应用伪标签，生成56,428个伪标注块。基准测试显示：语义分割模型平均交并比从65.93%提升至70.35%，实例分割模型平均精度从28.30%升至46.10%。结果表明，结合切块预处理与半监督学习可显著提升精细气孔结构分割效果。该框架支持气孔性状的大规模提取，推动人工智能驱动的作物表型分析普及。
原文摘要 · Abstract (English)
Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agriculture. Improving water-use efficiency in sorghum requires precise characterisation of stomatal traits, as stomata control of gas exchange, transpiration and photosynthesis have a major influence on crop performance. Automated analysis of sorghum stomata is difficult because the stomata are small (often less than 40 $μ$m in length in grasses such as sorghum) and vary in shape across genotypes and leaf surfaces. Automated segmentation contributes to high-throughput stomatal phenotyping, yet current methods still face challenges related to nested small structures and annotation bottlenecks. In this paper, we propose a semi-supervised instance segmentation framework tailored for analysis of sorghum stomatal components. We collect and annotate a sorghum leaf imagery dataset containing 11,060 human-annotated patches, covering the three stomatal components (pore, guard cell and complex area) across multiple genotypes and leaf surfaces. To improve the detection of tiny structures, we split high-resolution microscopy images into overlapping small patches. We then apply a pseudo-labelling strategy to unannotated images, producing an additional 56,428 pseudo-labelled patches. Benchmarking across semantic and instance segmentation models shows substantial performance gains: for semantic models the top mIoU increases from 65.93% to 70.35%, whereas for instance models the top AP rises from 28.30% to 46.10%. These results demonstrate that combining patch-based preprocessing with semi-supervised learning significantly improves the segmentation of fine stomatal structures. The proposed framework supports scalable extraction of stomatal traits and facilitates broader adoption of AI-driven phenotyping in crop science.
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