# An Iterative Convolutional Neural Network Algorithm Improves Electron Microscopy Image Segmentation

arXiv: https://arxiv.org/abs/1506.05849 · HTML: https://mlsanity.com/paper/1506.05849/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2015.

## Built on by (most cited first, 1 of 1)

- [ME-Net: Multi-Encoder Net Framework for Brain Tumor Segmentation](https://mlsanity.com/paper/2203.11213/) (2022)

## Abstract (arXiv)

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.