Neuron morphology reconstruction (tracing) in 3D volumetric images is critical for neuronal research. However, most existing neuron tracing methods are not applicable in challenging datasets where the neuron images are contaminated by noises or containing weak filament signals. In this paper, we present a two-stage 3D neuron segmentation approach via learning deep features and enhancing weak neuronal structures, to reduce the impact of image noise in the data and enhance the weak-signal neuronal structures. In the first stage, we train a voxel-wise multi-level fully convolutional network (FCN), which specializes in learning deep features, to obtain the 3D neuron image segmentation maps in an end-to-end manner. In the second stage, a ray-shooting model is employed to detect the discontinued segments in segmentation results of the first-stage, and the local neuron diameter of the broken point is estimated and direction of the filamentary fragment is detected by rayburst sampling algorithm. Then, a Hessian-repair model is built to repair the broken structures, by enhancing weak neuronal structures in a fibrous structure determined by the estimated local neuron diameter and the filamentary fragment direction. Experimental results demonstrate that our proposed segmentation approach achieves better segmentation performance than other state-of-the-art methods for 3D neuron segmentation. Compared with the neuron reconstruction results on the segmented images produced by other segmentation methods, the proposed approach gains 47.83% and 34.83% improvement in the average distance scores. The average Precision and Recall rates of the branch point detection with our proposed method are 38.74% and 22.53% higher than the detection results without segmentation.

中文翻译：

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通过学习深度特征和增强弱神经元结构进行神经元图像分割

3D 体积图像中的神经元形态重建（追踪）对于神经元研究至关重要。然而，大多数现有的神经元追踪方法不适用于具有挑战性的数据集，其中神经元图像被噪声污染或包含微弱的灯丝信号。在本文中，我们提出了一种通过学习深度特征和增强弱神经元结构的两阶段 3D 神经元分割方法，以减少数据中图像噪声的影响并增强弱信号神经元结构。在第一阶段，我们训练一个专门学习深度特征的体素多级全卷积网络（FCN），以端到端的方式获得 3D 神经元图像分割图。在第二阶段，采用射线射击模型检测第一阶段分割结果中不连续的片段，并通过射线暴采样算法估计断点的局部神经元直径并检测丝状片段的方向。然后，通过增强由估计的局部神经元直径和丝状片段方向确定的纤维结构中的弱神经元结构，构建 Hessian 修复模型来修复破损的结构。实验结果表明，我们提出的分割方法比其他最先进的 3D 神经元分割方法实现了更好的分割性能。与其他分割方法产生的分割图像上的神经元重建结果相比，所提出的方法在平均距离分数上分别提高了 47.83% 和 34.83%。使用我们提出的方法进行分支点检测的平均精度和召回率为 38。