Significance Robust segmentations of neurons greatly improve neuronal population reconstruction, which could support further study of neuron morphology for brain research. Aim Precise segmentation of 3D neuron structures from optical microscopy (OM) images is crucial to probe neural circuits and brain functions. However, the high noise and low contrast of images make neuron segmentation challenging. Convolutional neural networks (CNNs) can provide feasible solutions for the task but they require large manual labels for training. Labor-intensive labeling is highly expensive and heavily limits the algorithm generalization. Approach We devise a weakly supervised learning framework Docker-based deep network plus (DDeep3M+) for neuron segmentation without any manual labeling. A Hessian analysis based adaptive enhancement filter is employed to generate pseudo-labels for segmenting neuron images. The automated segmentation labels are input for training a DDeep3M to extract neuronal features. We mine more undetected weak neurites from the probability map based on neuronal structures, thereby modifying the pseudo-labels. We iteratively refine the pseudo-labels and retrain the DDeep3M model with the pseudo-labels to obtain a final segmentation result. Results The proposed method achieves promising results with the F1 score of 0.973, which is close to that of the CNN model with manual labels and superior to several segmentation algorithms. Conclusions We propose an accurate weakly supervised neuron segmentation method. The high precision results achieved on 3D OM datasets demonstrate the superior generalization of our DDeep3M+.

中文翻译：

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DDeep3M+：自适应增强支持神经元分割的弱监督学习。

意义 神经元的稳健分割极大地改善了神经元群的重建，这可以支持对大脑研究中神经元形态的进一步研究。目的 从光学显微镜 (OM) 图像中精确分割 3D 神经元结构对于探测神经回路和大脑功能至关重要。然而，图像的高噪声和低对比度使得神经元分割具有挑战性。卷积神经网络（CNN）可以为该任务提供可行的解决方案，但它们需要大量的手动标签进行训练。劳动密集型标记非常昂贵，并且严重限制了算法的泛化。方法我们设计了一个基于 Docker 的弱监督学习框架深度网络 plus (DDeep3M+)，用于神经元分割，无需任何手动标记。采用基于 Hessian 分析的自适应增强滤波器来生成用于分割神经元图像的伪标签。自动分割标签是用于训练 DDeep3M 以提取神经元特征的输入。我们从基于神经元结构的概率图中挖掘更多未检测到的弱神经突，从而修改伪标签。我们迭代地细化伪标签并使用伪标签重新训练 DDeep3M 模型以获得最终的分割结果。结果所提出的方法取得了可喜的结果，F1 得分为 0.973，接近于手动标签的 CNN 模型，并且优于几种分割算法。结论我们提出了一种准确的弱监督神经元分割方法。