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The neuron reconstruction from raw Optical Microscopy (OM) image stacks isthe basis of neuroscience. Manual annotation and semi-automatic neuron tracingalgorithms are time-consuming and inefficient. Existing deep learning neuronreconstruction methods, although demonstrating exemplary performance, greatlydemand complex rule-based components. Therefore, a crucial challenge isdesigning an end-to-end neuron reconstruction method that makes the overallframework simpler and model training easier. We propose a Neuron ReconstructionTransformer (NRTR) that, discarding the complex rule-based components, viewsneuron reconstruction as a direct set-prediction problem. To the best of ourknowledge, NRTR is the first image-to-set deep learning model for end-to-endneuron reconstruction. In experiments using the BigNeuron and VISoR-40datasets, NRTR achieves excellent neuron reconstruction results forcomprehensive benchmarks and outperforms competitive baselines. Results ofextensive experiments indicate that NRTR is effective at showing that neuronreconstruction is viewed as a set-prediction problem, which makes end-to-endmodel training available.

NRTR: Neuron Reconstruction with Transformer from 3D Optical Microscopy Images