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

• URL: http://arxiv.org/abs/2212.04163v1
• Date: Thu, 8 Dec 2022 09:35:22 GMT
• Title: NRTR: Neuron Reconstruction with Transformer from 3D Optical Microscopy Images
• Authors: Yijun Wang, Rui Lang, Rui Li and Junsong Zhang
• Abstract summary: We propose a Neuron Reconstruction Transformer (NRTR) that views neuron reconstruction as a direct set-prediction problem. NRTR is the first image-to-set deep learning model for end-to-end neuron reconstruction.
• Score: 5.724034347184251
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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