NeuroFly: An AI Framework for Whole-Brain Single Neuron Reconstruction

Neuroscience has advanced significantly, allowing us to understand the mapping of neurons in the brain. Neurons have dendrites and axons, branching structures that connect neurons. Understanding these mappings is crucial to discovering how the brain processes information, supports cognition, and controls movement, which has implications for neuroscience research and the treatment of neurological disorders. Mesoscale imaging is an advanced technique that has allowed us to understand these neural pathways. Despite being advanced, building a complete brain map and connectome is challenging as it is a very complex and time-consuming process. For example, a single mouse neuron can take up to 20 hours to map manually, making full brain-scale mapping for even one species nearly impossible without automated solutions. A team of researchers has developed an innovative framework, NeuroFly, that efficiently automates neural reconstruction.

Previous initiatives, such as the DIADEM challenge and the BigNeuron project, significantly advanced the neuron reconstruction process, but faced a major challenge with large-scale and complex data sets. The DIADEM challenge aimed to compare neural reconstruction algorithms using standardized data sets. However, it could not fully explain the scale-up needed to evaluate the terabytes of data for full brain reconstruction. The BigNeuron project, building on the DIADEM challenge, further standardized evaluation protocols and methods. The intricate neural details necessitated more real-world imaging scenarios that this algorithm could not accommodate.

NeuroFly's contributions are as follows:

1. Optimized segmentation, connection and review channel: NeuroFly formulates the neural reconstruction task as a structured workflow composed of three key stages:
   • Segmentation: Neural structures are isolated from surrounding brain tissue in the 3D image and identified using advanced automated techniques. These neuron fragments are not yet fully formed.
   • Connection: NeuroFly uses a 3D image-based path tracing method to connect these segments and form fully functional neurons. This technique also takes into account incomplete data or interruptions in the images.
   • Proofreading: This is the last but crucial step as humans review these segments and their respective connections to eliminate the chances of errors.

1. Extensible and model-specific data sets: Neural connections vary from species to species and in different regions of the brain. This led researchers to include diverse data sets, including various imaging methods and biological contexts. Additionally, data were collected following a strict protocol, allowing new species or imaging techniques to be added in the future.

1. Route based on 3D images below: Traditional methods struggled with the problem of gaps in the neural connectivity of the data. By using 3D image-based path tracing, NeuroFly has made it easier to construct incomplete neural pathways. This technique sends small virtual agents along the ends of each neuron segment, following signals from the surrounding image data. These signals help them connect to nearby segments or avoid background noise, ensuring that neural structures are more continuous and accurate, even when data is incomplete.

The NeuroFly results show the advantages of model-specific data sets to achieve high accuracy in different reconstruction scenarios. In testing, the framework achieved an average F1 score of 0.913 in reconstructing complex neural structures across multiple models, significantly outperforming generic datasets used in previous studies. NeuroFly's 3D path tracing method also effectively closes the gaps between neuron segments, forming a critical scheme for reducing reconstruction errors. This high precision accelerates the reconstruction of neurons and sets a new benchmark for future programs related to mapping neurons throughout the brain.

In conclusion, NeuroFly advances neuron reconstruction using model-specific data sets and a multi-step process that improves accuracy and scalability. The framework allows researchers to identify specific reconstruction problems by distinguishing between neuronal segmentation and connectivity errors. NeuroFly's contributions mark a step forward in neuron mapping, with implications for better understanding brain connectivity and function. As the framework continues to evolve, it is poised to become an essential tool for creating comprehensive connectomes, improving our understanding of the brain's intricate network.

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