Automated Proofreading of Digitally Reconstructed Neural Morphology Enhances Accuracy, Scalability, and...

Area of Science:

• Neuroscience
• Computational Biology
• Bioinformatics

Background:

• Large-scale neuroscience datasets necessitate automated, accurate, and standardized quality control (QC) for neural morphology.
• Manual proofreading of 3D neural morphology (SWC files) is labor-intensive, error-prone, and not scalable.
• Existing methods struggle with the volume and complexity of modern neuroscience data.

Purpose of the Study:

• To develop and evaluate a fully automated, machine-learning driven QC pipeline for neural reconstructions.
• To standardize neural morphologies, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons.
• To provide a scalable and reproducible framework for high-throughput neural morphology curation.

Main Methods:

• Developed an end-to-end, cloud-deployed pipeline integrating deterministic normalization, topology repair, geometric correction, and graph-based dendritic relabeling.
• Employed rule-based algorithms to detect and correct structural irregularities in SWC files.
• Utilized a graph convolutional network trained on 20,500 pyramidal neurons for accurate dendritic relabeling.

Main Results:

• The automated pipeline processed all neuronal reconstructions without manual intervention, restoring coherent and biologically accurate morphologies.
• Dendritic relabeling achieved a mean accuracy of 99.51% with high precision, recall, and F1-score.
• Distributed training demonstrated scalability and reproducibility, completing runs in approximately 25 hours.

Conclusions:

• A fully automated, cloud-scalable, open-source pipeline for standardizing neural reconstructions and performing accurate dendritic classification has been developed.
• Automated correction and relabeling procedures maintain geometric fidelity and compatibility with downstream analysis tools.
• This framework provides a robust foundation for large-scale neuroanatomical analysis and high-throughput morphology curation.