Proposition of Pseudo-labeling for Segmentation in Stacks of Electron Microscopy Images

Abstract

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 electron microscopy (EM) images for three-dimensional reconstruction, that is important process to observe tiny neuronal structures in detail. However, most of existing methods are based on supervised learning, that needs large amount of labeled dataset, whereas the number of labeled EM images is limited. To tackle this problem, 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. We experimented with two kinds of dataset, and showed that our method outperformed normal supervised learning with a few labeled samples, while the accuracy was not sufficient yet.