Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted widespread interest from researchers. Connectomics can be conducted at different spatial scales corresponding to the observational scale of brain imaging, these scales can be roughly divided into the microscale, mesoscale, and macroscale. This idea can inspire other researchers to optimize existing reconstruction algorithms and other areas of biological data analysis.Ĭonnectomics, a concept proposed by Sporns, aims to comprehensively map the structure of neuronal networks in the nervous system to improve our understanding of how the brain works. The intention of introducing connectivity consensus between different structures is to build a suitable optimization model that makes the reconstruction goals more consistent with biological plausible and domain knowledge. We present a joint optimization model by connectivity consensus to solve the neural structure agglomeration problem and demonstrate its superiority to existing methods. The experimental results on several public datasets demonstrate that the joint optimization model outperforms existing hierarchical agglomeration algorithms. The advantage of the optimization model is the simultaneous reconstruction of multiple structures in one optimization step.
We propose a joint graph partitioning model for solving ultrastructural and neuronal connections to overcome the limitations of connectivity cues at different levels. In this investigation, we introduce the concept of connectivity consensus between cellular and subcellular structures based on biological domain knowledge for neural structure agglomeration problems. The primary goal of this research is to construct a joint optimization framework to improve the accuracy and efficiency of neural structure reconstruction algorithms. Most existing reconstruction methodologies for cellular and subcellular structures are independent, and exploring the inter-relationships between structures will contribute to image analysis. Currently, the automated reconstruction algorithms in electron microscope volumes are in great demand. Nanoscale connectomics, which aims to map the fine connections between neurons with synaptic-level detail, has attracted increasing attention in recent years.
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