Automated Proofreading of Digitally Reconstructed NeuralMorphology Enhances Accuracy, Scalability, and Standardization
This paper presents a fully automated, cloud-scalable, and open-source pipeline that utilizes machine learning and rule-based algorithms to standardize, correct structural anomalies, and accurately relabel dendritic trees in large-scale 3D neural reconstructions, thereby enhancing the accuracy, efficiency, and reproducibility of neuroanatomical data quality control.