Implicit boundary learning for connectomics

Abstract

Segmentation of complete neurons in 3D electron microscopy images is an important task in Connectomics. A common approach for automatic segmentation is to detect membrane between neurons in a first step. This is often done with a random forest. We propose a new implicit boundary learning scheme that optimizes the segmentation error of neurons instead of the classification error of membrane. Given a segmentation, optimal labels for boundary between neurons and for non-boundary are found automatically and are used for training. In contrast to training random forests with labels for membrane and intracellular space, this novel training method does not require many labels for the difficult to label membrane and reduces the segmentation error significantly.