An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation

Understand

To build the connectomics map of the brain, we developed a new algorithm that can automatically refine the Membrane Detection Probability Maps (MDPM) generated to perform automatic segmentation of electron microscopy (EM) images.

• To achieve this, we executed supervised training of a convolutional neural network to recover the removed center pixel label of patches sampled from a MDPM.
• MDPM can be generated from other machine learning based algorithms recognizing whether a pixel in an image corresponds to the cell membrane.
• By iteratively applying this network over MDPM for multiple rounds, we were able to significantly improve membrane segmentation results.