Precise Decision Energized Collaborative Strategies to Achieve High-Quality and Large-Scale Neuronal Reconstruction.

Abstract

The brain is the least explored organ in the human body. Brain functions are realized through a complex neural network composed of a vast number of neurons, and understanding the morphology of these neurons is base to brain studies. However, obtaining high-quality, large-scale data on neuron morphology remains a significant challenge. In this study, we propose a precise data-graded allocation method for neuron reconstruction, the accuracy is safeguarded by the allocation algorithm and the quantitative model. Reconstruction efficiency was improved by optimizing automated reconstruction algorithm, human-machine interaction workflow and human-task matching method. We have implemented this strategy on a web-based platform, and the results show that 92.9% of image data can be easily reconstructed, thereby reducing the skill requirements for participant. The reconstruction accuracy is 98.2%$\pm$3.1%, better than existing methods. We also provides meticulously annotated datasets that can propel significant advancements in artificial intelligence technology. In addition, we can offer a well-balance across quality, cost, and efficiency, sharing a more flexible and versatile solution for three-dimensional neuron reconstruction.