Digital reconstruction or tracing of 3D tree-like neuronal structures from optical microscopy images is essential for understanding the functionality of neurons and reveal the connectivity of neuronal networks. Despite the existence of numerous tracing methods, reconstructing a neuron from highly noisy images remains challenging, particularly for neurites with low and inhomogeneous intensities. Conducting deep convolutional neural network (CNN)-based segmentation prior to neuron tracing facilitates an approach to solving this problem via separation of weak neurites from a noisy background. However, large manual annotations are needed in deep learning-based methods, which is labor-intensive and limits the algorithm's generalization for different datasets. In this study, we present a weakly supervised learning method of a deep CNN for neuron reconstruction without manual annotations. Specifically, we apply a 3D residual CNN as the architecture for discriminative neuronal feature extraction. We construct the initial pseudo-labels (without manual segmentation) of the neuronal images on the basis of an existing automatic tracing method. A weakly supervised learning framework is proposed via iterative training of the CNN model for improved prediction and refining of the pseudo-labels to update training samples. The pseudo-label was iteratively modified via mining and addition of weak neurites from the CNN predicted probability map on the basis of their tubularity and continuity. The proposed method was evaluated on several challenging images from the public BigNeuron and Diadem datasets, to fMOST datasets. Owing to the adaption of 3D deep CNNs and weakly supervised learning, the presented method demonstrates effective detection of weak neurites from noisy images and achieves results similar to those of the CNN model with manual annotations. The tracing performance was significantly improved by the proposed method on both small and large datasets (>100 GB). Moreover, the proposed method proved to be superior to several novel tracing methods on original images. The results obtained on various large-scale datasets demonstrated the generalization and high precision achieved by the proposed method for neuron reconstruction.

中文翻译：

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用于神经元重建的 3D 深度网络的弱监督学习。


从光学显微镜图像中数字重建或追踪 3D 树状神经元结构对于理解神经元的功能和揭示神经元网络的连接性至关重要。尽管存在多种追踪方法，但从高噪声图像中重建神经元仍然具有挑战性，特别是对于强度低且不均匀的神经突而言。在神经元追踪之前进行基于深度卷积神经网络 (CNN) 的分割有助于通过将弱神经突与噪声背景分离来解决该问题。然而，基于深度学习的方法需要大量的手动注释，这是劳动密集型的，并且限制了算法对不同数据集的泛化。在本研究中，我们提出了一种无需手动注释的深度 CNN 弱监督学习方法，用于神经元重建。具体来说，我们应用 3D 残差 CNN 作为判别神经元特征提取的架构。我们基于现有的自动跟踪方法构建神经元图像的初始伪标签（无需手动分割）。通过 CNN 模型的迭代训练提出了一种弱监督学习框架，以改进预测和细化伪标签以更新训练样本。根据 CNN 预测概率图的管状性和连续性，通过挖掘和添加弱神经突来迭代修改伪标签。所提出的方法在来自公共 BigNeuron 和 Diadem 数据集到 fMOST 数据集的几个具有挑战性的图像上进行了评估。 由于采用 3D 深度 CNN 和弱监督学习，所提出的方法可以有效地从噪声图像中检测出弱神经突，并取得与手动注释的 CNN 模型类似的结果。所提出的方法在小型和大型数据集（> 100 GB）上的跟踪性能均得到显着提高。此外，事实证明，所提出的方法优于原始图像上的几种新颖的追踪方法。在各种大规模数据集上获得的结果证明了所提出的神经元重建方法所实现的泛化性和高精度。