Semi-Supervised Learning for Electron Microscopy Image Segmentation


  • Eichi Takaya Keio University
  • Yusuke Takeichi Keio University
  • Mamiko Ozaki Keio University
  • Satoshi Kurihara Keio University



In the research field called connectomics, it is aimed to investigate the structure and connection of the neural system in the brain and sensory organ of the living things. Earlier studies have been proposed the method to help experts who suffer from labeling for three-dimensional reconstruction, that is important process to observe tiny neuronal structure in detail. In this paper, we proposed semi-supervised learning method, that performs pseudo-labeling. This makes it possible to automatically segment neuronal regions using only a small amount of labeled data. Experimental result showed that our method outperformed normal supervised learning with few labeled samples, while the accuracy was not sufficient yet.




How to Cite

Takaya, E., Takeichi, Y., Ozaki, M., & Kurihara, S. (2019). Semi-Supervised Learning for Electron Microscopy Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 10047-10048.



Student Abstract Track