End-to-End Neuron Tree Tracing with Historical Trajectories

Reconstructing neuronal trees from 3-D microscopy requires recovering branching connectivity from weak and ambiguous local image evidence. A false junction or missed branch can corrupt an entire downstream arbor. We introduce TreeTracer, an end-to-end autoregressive model that expands a point-and-direction prompt into a neuronal subtree. An elegant end-to-end design enables the joint optimization of neuronal tree geometry and topology predictions through gradient-based learning. A causal decoder encodes historical trajectory features for more accurate topology prediction and employs local–global cross-attention to extract visual evidence more efficiently. On block-level subtree reconstruction, TreeTracer achieved Fiber F1 scores of 58.4% on CWMBS and 66.4% on CORAL, compared with $51.8\%$ and $61.0\%$ for the strongest competing methods, respectively. On brain-wide neuron reconstruction, it reached 41.6%, compared with 35.1% for the strongest competing method.