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

Background: The rapid expansion of large-scale neuroscience datasets has increased the need for automated, accurate, and standardized quality control (QC). Manual proofreading of 3-dimensional neural morphology (SWC files) remains labor-intensive, error-prone, and non-scalable. We developed and evaluated a fully automated, machine-learning–driven QC pipeline to standardize neural reconstructions, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons. Methods: We developed an end-to-end, cloud-deployed pipeline for automated QC, correction, and standardization of SWC-formatted neural morphologies. The framework integrates deterministic structural normalization, topology repair, geometric correction, quantitative morphometric analysis, and graph-based dendritic relabeling within a containerized React/Flask architecture deployed on Amazon Web Services. Rule-based algorithms systematically detect, classify, and correct structural irregularities including overlapping nodes, spurious side branches, non-positive radii, disconnected components, and anomalously long parent-child connections. A graph convolutional network, trained on Sholl-derived features from 20,500 pyramidal neurons, performs dendritic relabeling. Model training employed an 80/10/10 train–validation–test split with adaptive learning-rate scheduling and distributed execution across ten runs to evaluate stability and reproducibility. The pipeline generates images of the final product and computes quantitative morphometrics using L-Measure. Results: All neural reconstructions were processed without manual intervention. Automated normalization and topology repair improved structural consistency and standardized neuronal morphologies for quantitative analysis and visualization while preserving topological and morphological continuity throughout the correction process. Dendritic relabeling achieved a test classification accuracy of 99.51%, calculated from 2040 correctly classified neurons out of 2050 test neurons (2040/2050 × 100 = 99.51%). The test dataset comprised a separate 10% partition of the 20,500-neuron dataset, resulting from the 80/10/10 training–validation–test split, with no overlap between the three subsets. The mean validation accuracy across ten training runs also reached 99.51% at the final epoch, with class-weighted precision of 0.978 and recall of 0.977. Validation accuracy increased rapidly during the initial epochs, exhibited a transient decrease at epoch 15 and subsequently recovered and stabilized by epoch 50. Enforcing a single apical dendritic tree per neuron improved anatomical consistency without reducing classification performance. Distributed training completed all runs in approximately 25 hours, demonstrating scalability and reproducibility for large datasets. Conclusions: We present an automated, cloud-scalable open-source pipeline for standardizing neural reconstructions and performing biologically consistent dendritic classification with high accuracy. The automated correction and relabeling procedures do not alter or compromise the size or unaffected morphological detail of the original SWC files, ensuring geometric fidelity and compatibility with downstream analysis tools. This open-access framework provides a robust foundation for high-throughput neural morphology curation and large-scale neuroanatomical analysis.