Neuron geometry extraction by perceptual grouping in ssTEM images

In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.