![]() ![]() The segmentation framework, referred to as “tubularity flow field (TuFF)”, performs directional regional growing guided by the direction of tubularity of the neurites. In, an automated scheme to perform segmentation in a variational framework was proposed to trace neurons from confocal microscopy images. The algorithm can handle complex structures adaptively and optimize the localization of bifurcations. proposed an algorithm for automatic neuron reconstruction. The recent advances in biomedical imaging have allowed the initial development of computer-aided semiautomatic or automatic approaches to detect dendritic spines based on image analysis. Therefore, manual methods are not suitable for the processing of large-scale data. Meanwhile, different criteria may lead to different results. Manual validation is extremely time-consuming, and error prone. The advent of boutons and spines can be imaged in live animals over days or even months, allowing observation of structural changes in vivo, often in direct association with learning. Brain imaging can be used to characterize changes occurring in a brain during very different time-scales. The structural plasticity of boutons and spines underlies functional synaptic plasticity, widely accepted as the neural basis of learning and memory. Chemical synapses, especially excitatory synapses, typically consist of presynaptic axon boutons and postsynaptic dendritic spines. In the mammalian central nervous system, the vast majority of the synapses are chemical. There are two major types of synapses: chemical and electrical. Synapses were first discovered in the 1890s, when Sir Sherrington, through his pioneering work on motor reflexes, wrote that synapse is the way of neuronal communication in the nervous system. This approach will helpful for neuroscientists to automatically analyze and quantify the formation, elimination and destabilization of the axonal boutons, dendritic spines and synapses. Experimental results were validated manually, and the effectiveness of our proposed method was demonstrated. In this study, we first detected the axonal boutons and dendritic spines respectively, and then identified synapses based on these results. Then, synapses can be identified by integrating information from adjacent axon boutons and dendritic spines. They can be detected automatically using this image processing method. Axon boutons and dendritic spines are structurally distinct. We present an automated approach for the identification of synaptic structures in two-photon images. However, the identification and quantification of structural imaging data currently rely heavily on manual annotation, a method that is both time-consuming and prone to bias. Two-photon laser scanning microscopy allows the visualization of synaptic structures in vivo, leading to many important findings. Therefore, they have always been a major focus of neuroscience research. The size, morphology, and connectivity of these synapses are significant in determining the functional properties of the neural network. In the nervous system, the neurons communicate through synapses. ![]()
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