Zhang, L.; Huang, L.; Yuan, Z.; Hang, Y.; Zeng, Y.; Li, K.; Wang, L.; Zeng, H.; Chen, X.; Zhang, H.; Xi, J.; Chen, D.; Gao, Z.; Le, L.; Chen, J.; Ye, W.; Liu, L.; Wang, Y.; Peng, H.

Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is widely recognized as a crucial challenge in both individual research laboratories and large-scale scientific projects focusing on cell types and brain anatomy. This task often fails both conventional manual reconstruction and state-of-the-art automatic reconstruction algorithms, even many of which are based on artificial intelligence (AI). It is also critical but challenging to organize multiple neuroanatomists to produce and cross-validate biologically relevant and agreeable reconstructions in scaled data production. Here we propose an approach based on collaborative human intelligence augmented by AI. Specifically, we have developed a Collaborative Augmented Reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative-editing of neuron anatomy using a variety of client devices, such as desktop workstations, virtual reality headsets, and mobile phones, enabling users to contribute anytime and anywhere and take advantage of several AI-based automation tools. We have tested CAR\'s applicability for challenging mouse and human neurons towards a scaled and faithful data production. Our data indicate that the CAR platform is suitable for generating tens of thousands of neuronal reconstructions used in our companion studies.