The morphology reconstruction (tracing) of neurons in 3D microscopy images is important to neuroscience research. However, this task remains very challenging because of the low signal-to-noise ratio (SNR) and the discontinued segments of neurite patterns in the images. In this paper, we present a neuronal structure segmentation method based on the ray-shooting model and the Long Short-Term Memory (LSTM)-based network to enhance the weak-signal neuronal structures and remove background noise in 3D neuron microscopy images. Specifically, the ray-shooting model is used to extract the intensity distribution features within a local region of the image. And we design a neural network based on the dual channel bidirectional LSTM (DC-BLSTM) to detect the foreground voxels according to the voxel-intensity features and boundary-response features extracted by multiple ray-shooting models that are generated in the whole image. This way, we transform the 3D image segmentation task into multiple 1D ray/sequence segmentation tasks, which makes it much easier to label the training samples than many existing Convolutional Neural Network (CNN) based 3D neuron image segmentation methods. In the experiments, we evaluate the performance of our method on the challenging 3D neuron images from two datasets, the BigNeuron dataset and the Whole Mouse Brain Sub-image (WMBS) dataset. Compared with the neuron tracing results on the segmented images produced by other state-of-the-art neuron segmentation methods, our method improves the distance scores by about 32% and 27% in the BigNeuron dataset, and about 38% and 27% in the WMBS dataset.

中文翻译：

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通过射线射击模型和DC-BLSTM网络进行3D神经元显微镜图像分割。

3D显微镜图像中神经元的形态重建（追踪）对神经科学研究至关重要。但是，由于低信噪比（SNR）和图像中的神经突图样的不连续片段，该任务仍然非常具有挑战性。在本文中，我们提出了一种基于射线拍摄模型和基于长短期记忆（LSTM）的网络的神经元结构分割方法，以增强弱信号神经元结构并消除3D神经元显微图像中的背景噪声。具体地，射线拍摄模型用于提取图像的局部区域内的强度分布特征。我们设计了一个基于双通道双向LSTM（DC-BLSTM）的神经网络，根据整个图像中生成的多个射线拍摄模型提取的体素强度特征和边界响应特征来检测前景体素。这样，我们将3D图像分割任务转换为多个1D射线/序列分割任务，这比许多现有的基于卷积神经网络（CNN）的3D神经元图像分割方法更容易标记训练样本。在实验中，我们评估了我们的方法在来自两个数据集（BigNeuron数据集和整个小鼠脑子图像（WMBS）数据集）的具有挑战性的3D神经元图像上的性能。与其他先进的神经元分割方法产生的分割图像上的神经元追踪结果相比，