DeepBranchAI: A Novel Cascade Workflow Enabling Accessible 3D Branching Network Segmentation

Three-dimensional branching networks exist throughout biological, natural, and man-made systems as pathways through volumetric space. Segmentation is required to correctly reconstruct the networks in whole or in part for analysis. This presents a unique challenge as minor voxel misclassifications can cause sporadic connectivity shifts, whereby connected elements appear to disconnect (false negatives) or to even become amplified (false positives). Addressing this topological vulnerability requires the generation of 3D models since 2D slice-by-slice approaches cannot maintain connectivity across x, y, and z axes. Yet tracking 3D architecture demands substantially more analytical resources than using a 2D strategy as generating volumetric annotations requires extraordinary amounts of expert time to manually annotate. This creates a fundamental annotation bottleneck: with sparse training data available, deep learning models tend to overfit available volumes and fail to generalize to novel volumes. We present a cascade training workflow that overcomes this bottleneck through a positive feedback loop in which trained models become annotation aids for subsequent volumes. The workflow begins with random forests that generate initial drafts from minimal labels, followed by expert refinement that cycle ever closer to the ground truth. As refined data accumulates, training transitions from 2D to 3D architectures, which systematically expand sparse datasets into comprehensive training sets. The outcome is a 3D nnU-Net model optimized for topology-preserving segmentation. We dub our resulting model DeepBranchAI. Training validation on heavily branching mitochondrial networks, generated by focused ion beam scanning electron microscopy (FIB-SEM, 15nm voxel resolution) achieved Dice Similarity Coefficient (DSC) = 0.942 across 5-fold cross-validation. Transfer learning to vascular networks (VESSEL12 dataset, CT volumes, 30,000-fold voxel size difference) achieved 97.05% accuracy against ground truth, validating that learned features represent domain-general topological principles. This workflow reduces annotation time from months to weeks while transforming sparse initial labels into robust training sets. Complete implementation, trained weights, and validation code are provided open source.