构建时序对齐的合成真菌图像数据集，助力深度学习研究真菌生长过程。
Synthetic Fungi Datasets: A Time-Aligned Approach
- 通过可控生成实现真菌生长各阶段的时间对齐建模。
- 涵盖孢子缩小、分枝动态与菌丝网络形成等关键变化特征。
- 适合从事真菌形态分析、农业病害监测与生物医学研究的团队使用。
真菌在其生命周期中经历动态形态变化，从孢子发育为成熟的菌丝结构并形成复杂网络。为支持此类时变过程的研究，我们提出一个合成的时序对齐图像数据集，系统刻画了真菌生长的关键阶段。该数据集准确捕捉孢子尺寸减小、分枝动力学及复杂菌丝网络的涌现等现象。受控生成流程确保了时间一致性、可扩展性与结构对齐，克服了真实真菌数据集的局限性。该数据集专为深度学习应用优化，可用于生长阶段分类、真菌发育预测与形态模式时序分析。其应用场景涵盖农业、医学与工业真菌学，为自动化真菌分析、疾病监测及人工智能驱动的真菌生物学研究提供坚实基础。
原文摘要 · Abstract (English)
Fungi undergo dynamic morphological transformations throughout their lifecycle, forming intricate networks as they transition from spores to mature mycelium structures. To support the study of these time-dependent processes, we present a synthetic, time-aligned image dataset that models key stages of fungal growth. This dataset systematically captures phenomena such as spore size reduction, branching dynamics, and the emergence of complex mycelium networks. The controlled generation process ensures temporal consistency, scalability, and structural alignment, addressing the limitations of real-world fungal datasets. Optimized for deep learning (DL) applications, this dataset facilitates the development of models for classifying growth stages, predicting fungal development, and analyzing morphological patterns over time. With applications spanning agriculture, medicine, and industrial mycology, this resource provides a robust foundation for automating fungal analysis, enhancing disease monitoring, and advancing fungal biology research through artificial intelligence.
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