  The neuron reconstruction from raw Optical Microscopy (OM) image stacks is
the basis of neuroscience. Manual annotation and semi-automatic neuron tracing
algorithms are time-consuming and inefficient. Existing deep learning neuron
reconstruction methods, although demonstrating exemplary performance, greatly
demand complex rule-based components. Therefore, a crucial challenge is
designing an end-to-end neuron reconstruction method that makes the overall
framework simpler and model training easier. We propose a Neuron Reconstruction
Transformer (NRTR) that, discarding the complex rule-based components, views
neuron reconstruction as a direct set-prediction problem. To the best of our
knowledge, NRTR is the first image-to-set deep learning model for end-to-end
neuron reconstruction. In experiments using the BigNeuron and VISoR-40
datasets, NRTR achieves excellent neuron reconstruction results for
comprehensive benchmarks and outperforms competitive baselines. Results of
extensive experiments indicate that NRTR is effective at showing that neuron
reconstruction is viewed as a set-prediction problem, which makes end-to-end
model training available.
