Reducing manual operation time to obtain a segmentation learning model for volume electron microscopy using stepwise deep learning with manual correction.

Three-dimensional (3D) observation of a biological sample using serial-section electron microscopy is widely used. However, organelle segmentation requires a significant amount of manual time. Therefore, several studies have been conducted to improve organelle segmentation's efficiency.

Practical method of cell segmentation in electron microscope image stack using deep convolutional neural network☆.

Segmentation of three-dimensional (3D) electron microscopy (EM) image stacks is an arduous and tedious task. Deep convolutional neural networks (CNNs) work well to automate the segmentation; however, they require a large training dataset, which is a major impediment. In order to solve this issue, especially for sparse segmentation, we used a CNN with a minimal training dataset.