Digital neuron reconstruction from 3D microscopy images is an essential technique for investigating brain connectomics and neuron morphology. Existing reconstruction frameworks use convolution-based segmentation networks to partition the neuron from noisy backgrounds before applying the tracing algorithm. The tracing results are sensitive to the raw image quality and segmentation accuracy. In this paper, we propose a novel framework for 3D neuron reconstruction. Our key idea is to use the geometric representation power of the point cloud to better explore the intrinsic structural information of neurons. Our proposed framework adopts one graph convolutional network to predict the neural skeleton points and another one to produce the connectivity of these points. We finally generate the target SWC file through the interpretation of the predicted point coordinates, radius, and connections. Evaluated on the Janelia-Fly dataset from the BigNeuron project, we show that our framework achieves competitive neuron reconstruction performance. Our geometry and topology learning of point clouds could further benefit 3D medical image analysis, such as cardiac surface reconstruction. Our code is available at https://github.com/RunkaiZhao/PointNeuron.
翻译:从 3D 显微镜图像中重建数字神经神经是调查大脑连接和神经形态学的一项必要技术。 现有的重建框架使用基于革命的分化网络,在应用追踪算法之前将神经神经从吵闹的背景中隔开。 追踪结果对原始图像质量和分化准确性十分敏感。 在本文中,我们提出一个3D 神经重建的新框架。 我们的关键想法是利用点云的几何表示力更好地探索神经元的内在结构信息。 我们提议的框架采用一个图形共变网络来预测神经骨骼点,另一个网络来产生这些点的连接性。 我们最终通过对预测点坐标、半径和连接的解释生成目标 SWC文件。 我们从大Neuron项目中评估了Janelia-Fly数据集, 我们展示了我们的框架取得了有竞争力的神经重建性表现。 我们的点云的几何和地形学研究可以进一步有益于3D 医学图像分析, 如心脏表面重建。 我们的代码可以在 https://github.com/Runkaize/Pogen Neuron。