Advances in 3-dimensional electron microscopy (3D-EM) have made possible imaging brain ultrastructures with nanoscopic resolutions from hundreds of micrometers tissue. To quantify the acquired big datasets, it requires automated segmentation techniques that annotate the entirety of ultrastructural components. Recently, deep convolutional neural networks (DCNNs) have emerged as the key to automated human-level segmentation of neurites in 3D-EM datasets. Current techniques, however, address the segmentation of high-resolution datasets and for small numbers of neurites. Therefore, we present DeepACSON.DeepACSON performs DCNN-based semantic segmentation and 3D shape analysis based instance segmentation. We used DeepACSON to segment ten low-resolution serial block-face scanning electron microscopy datasets of the white matter of rats after sham-operation and traumatic brain injury. DeepACSON segmented hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphological aspects of myelinated axons, such as axonal diameter, eccentricity, and tortuosity, as well as the spatial organization of ultrastructures, such as inter-mitochondrial distance, and the density of cells and myelinated axons.15 × 15 × 15 µm 3 white matter ultrastructures requires 2 400 h of an expert's time 4 . Semi-automated segmentation methods based on machine learning approaches 3, 5 have improved the rate of segmentation. However, these methods still require a considerable amount of manual interaction as the segmentation is driven on the manually extracted skeletons of neurites, proofreading, and correction of errors.1 Deep convolutional neural networks (DCNNs) have emerged as the key to the state-of-the-art automated segmentation techniques 6-9 . DCNN-based segmentation methods generally comprise two steps: generating a membrane probability map from 3D-EM datasets referred to as semantic segmentation, followed by instance segmentation that turns probability maps into individual object instances. DCNNs are typically used for semantic segmentation and more traditional image analysis techniques favoring bottom-up design, i.e., over-segmentation and subsequent merge, are used for instance segmentation. For example, DeepEM3D 7 applied 3D watershed transform on the membrane probability maps. Funke et al. 8 proposed learning voxel affinities, followed by 3D watershed and iterative region agglomeration on the watershed segmentation. Only recently, Januszewski et al. 9 suggested flood-filling networks merging the semantic and instance segmentation into a recurrent neural network. Flood-filling networks have a bottom-up design, offering a sophisticated training-based alternative for over-segmentation and subsequent merge.Bottom-up design of the state-of-the-art EM segmentation techniques 6-9 is subjected to local optimization. Hence, segmentation requires distinctive image features such as high contrast between cellular membranes and other struc...