Large scale phase-contrast images taken at high resolution through the life of a cultured neuronal network are analyzed by a graph-based unsupervised segmentation algorithm with a very low computational cost, scaling linearly with the image size. The processing automatically retrieves the whole network structure, an object whose mathematical representation is a matrix in which nodes are identified neurons or neurons' clusters, and links are the reconstructed connections between them. The algorithm is also able to extract any other relevant morphological information characterizing neurons and neurites. More importantly, and at variance with other segmentation methods that require fluorescence imaging from immunocytochemistry techniques, our non invasive measures entitle us to perform a longitudinal analysis during the maturation of a single culture. Such an analysis furnishes the way of individuating the main physical processes underlying the selforganization of the neurons' ensemble into a complex network, and drives the formulation of a phenomenological model yet able to describe qualitatively the overall scenario observed during the culture growth. V C 2014 International Society for Advancement of Cytometry Key terms neuron image segmentation; cultured neuronal network; neurite tracing; complex networks; network topology analysis; automated tracing; high throughput; neuronal morphology; light microscopy; connectome reconstruction ALONG the past decades, cultured neuronal networks (CNNs) have constituted a fundamental tool for scientists, as one of the benchmark models for the study of the central nervous system. They, indeed, allow performing very well controlled laboratory experiments, thus providing a systematic way to approach fundamental questions, as for example, unveiling the principles and mechanisms underlying memory, connectivity, and even information processing of their in vivo counterparts (1-5).CNNs have also important practical applications, when computer-connected to a real or a simulated robot (to create what is called a hybrot (6-8) or an animat (9,10), respectively), in that scientists are then endowed with the possibility of studying some basic neuronal processes in realistic contexts, such as learning and plasticity. Possibly, the most relevant advantage of CNNs is the unique option they offer of following the footprints of the self (or induced) organization of the network's functionality and dynamics (usually by means of a multi-electrode array [MEA] or calcium fluorescence, recording the CNN electrophysiological data, or inducing electrical stimulations in given spatial positions) together with the monitoring and tracking of the structural organization of the neurons' connectivity along the entire course of the culture's growth (11-13).Although culturing neurons on top of a MEA equipped chamber implicates, in general, only mild constraints, following the development of the culture's structure is
