  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.
