菌丝体扫描电镜显微图中的伪影挖掘
Mining Artifacts in Mycelium SEM Micrographs
AI总结：
本研究针对菌丝体扫描电镜显微图的伪影识别问题，结合监督与无监督机器学习方法实现伪影自动识别，为菌丝体微结构表征提供技术支持。
摘要：
菌丝体是一种基于真菌菌丝体的极具潜力的生物材料，具有高度多孔的纳米纤维结构。扫描电子显微图用于表征其网络，但现有的纳米纤维微结构工具未考虑生物材料的特殊性。采用人工纳米纤维软件对菌丝体进行表征，会在分析中引入成像伪影形成的不确定性。本研究结合监督与无监督机器学习方法，实现对菌丝体微结构 mapped 孔隙中伪影的自动识别。
英文摘要：
Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous in mycelium characterization adds the uncertainty of imaging artifact formation to the analysis. The reported work combines supervised and unsupervised machine learning methods to automate the identification of artifacts in the mapped pores of mycelium microstructure. Keywords: Machine learning; unsupervised learning; image processing; mycelium; microstructure informatics
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