Neurons contain three compartments, the soma, long axon, and dendrites, which have distinct energetic and biochemical requirements. Mitochondria feature in all compartments and regulate neuronal activity and survival, including energy generation and calcium buffering alongside other roles including proapoptotic signaling and steroid synthesis. Their dynamicity allows them to undergo constant fusion and fission events in response to the changing energy and biochemical requirements. These events, termed mitochondrial dynamics, impact their morphology and a variety of three-dimensional (3D) morphologies exist within the neuronal mitochondrial network. Distortions in the morphological profile alongside mitochondrial dysfunction may begin in the neuronal soma in ageing and common neurodegenerative disorders. However, 3D morphology cannot be comprehensively examined in flat, two-dimensional (2D) images. This highlights a need to segment mitochondria within volume data to provide a representative snapshot of the processes underpinning mitochondrial dynamics and mitophagy within healthy and diseased neurons. The advent of automated high-resolution volumetric imaging methods such as Serial Block Face Scanning Electron Microscopy (SBF-SEM) as well as the range of image software packages allow this to be performed.We describe and evaluate a method for randomly sampling mitochondria and manually segmenting their whole morphologies within randomly generated regions of interest of the neuronal soma from SBF-SEM image stacks. These 3D reconstructions can then be used to generate quantitative data about mitochondrial and cellular morphologies. We further describe the use of a macro that automatically dissects the soma and localizes 3D mitochondria into the subregions created.