A Mycelium Dataset with Edge-Precise Annotation for Semantic Segmentation

Abstract

With the increasing application of computer vision in mycology research, precisely segmenting mycelium and its edges in petri dish images remains a critical and underexplored task. This technology, accurately delineating mycelium boundaries, enables quantification of growth patterns, playing a crucial role in exploration of strain-related features, environmental adaptability, and physiological stimuli responses. The field confronts two bottlenecks, restricting real-world computer vision application. First, scarce public datasets impede development of mycelium-specific algorithms. Second, low contrast and high complexity of mycelium edges complicate annotation and segmentation processes. To address these bottlenecks, we established MyceliumSeg, the first large-scale benchmark dataset. MyceliumSeg contains: (i) 20,176 high-quality diverse images covering full growth cycle of four fungal species across multiple culture conditions; (ii) 567 pixel-level labeled samples generated with 37 person-days' manual effort through a mycelium annotation framework, including a multi-blind refined annotation guideline and a novel disagreement solution; (iii) a benchmark evaluating mainstream deep learning models under classic and boundary-aware segmentation metrics. MyceliumSeg serves as valuable resource for research on both mycology and segmentation algorithm.

Affiliated Institutions

• Sanya University CN
• Jilin Agricultural University CN
• Beijing Academy of Artificial Intelligence CN
• Jilin University CN
• Shanghai Innovative Research Center of Traditional Chinese Medicine CN
• Wuhan University of Technology CN
• Shanghai Artificial Intelligence Laboratory
• Wuhan College CN

Related Publications