Automated segmentation of synapses in 3D EM data

Abstract

This contribution presents a method for automatic detection of excitatory, asymmetric synapses and segmentation of synaptic junctional complexes in stacks of serial electron microscopy images with nearly isotropic resolution. The method uses a Random Forest classifier in the space of generic image features, computed directly in the 3D neighborhoods of each pixel, and an additional step of interactive probability maps thresholding. On the test dataset, the algorithm missed considerably less synapses than the human expert during the ground truth creation, while maintaining an equivalent false positive rate. The algorithm is implemented as an extension to the Interactive Learning and Segmentation Toolkit “ilastik” and is freely available on our website (www.ilastik.org/synapse-detection).