论文标题
NRTR：来自3D光学显微镜图像的变压器的神经元重建
NRTR: Neuron Reconstruction with Transformer from 3D Optical Microscopy Images
论文作者
论文摘要
来自原始光学显微镜（OM）图像堆栈的神经元重建是神经科学的基础。手动注释和半自动神经元追踪算法是耗时且效率低下的。现有的深度学习神经元重建方法尽管表明了示例性的性能，但极大地要求基于规则的组件。因此，至关重要的挑战是设计一种端到端的神经元重建方法，使整体框架更简单，模型训练更加容易。我们提出了一个神经元重建变压器（NRTR），该变压器（NRTR）丢弃基于复杂的规则的组件，将神经元重建视为直接的设定预测问题。据我们所知，NRTR是端到端神经元重建的第一个图像到设定的深度学习模型。在使用bigneuron和visor-40数据集的实验中，NRTR可为全面的基准测试和优于竞争基线而实现出色的神经元重建结果。广泛实验的结果表明，NRTR有效地表明神经元重建被视为设定的预测问题，这使得端到端模型训练可用。
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.
