Automatically segmenting and reconstructing neurons in SEM images

Neuronal networks reconstruction is a big challenge in the neuroscience. Recent developments in volume electron microscopy (EM) imaging have enabled us to obtain large amounts of brain tissues image data. Analysis of the tremendously huge neuronal EM images based on automated method would be of vital importance. In this paper, we propose a method that Deep Convolutional Neural Network (DCNN) is used for neuronal boundary detection; and then, with the membrane detection probability map (MDPM) generated by DCNN, a marker-controlled watershed method is applied to segment neurons in the EM images over the MDPM. Semi-automated and fully automated 3D reconstruction methods are employed to connect the sections of the corresponding segmentations belonging to each neuron. Finally, we have reconstructed dense neurons in 8000×8000×1796 consecutive EM images stack of drosophila mushroom body with resolution of 4nm×4nm×50nm with automated method and several sparse neurons with semi-automated method.