Background

Segmentation of electron microscopic continuous section images by deep learning has attracted attention as a technique to reduce the cost of annotation for researchers attempting to make observations using 3D reconstruction methods. However, when the observed samples are rare, or scanning circumstances are unstable, pursuing generalization performance for newly obtained samples is not appropriate.

New methods

We assume a transductive setting that predicts all labels in a dataset from only partially obtained labels while avoiding the pursuit of generalization performance for unknown data. Then, we propose sequential semi-supervised segmentation (4S), which semi-automatically extracts neural regions from electron microscopy image stacks. This method focuses on the fact that adjacent images have a strong correlation in serial images. Our 4S repeats training, inference, and pseudo-labeling using a minimal number of teacher labels and performs segmentation on all slices.

Result

Our experiments using two types of serial section images showed effectiveness in terms of both quality and quantity. In addition, we experimentally clarified the effect of the number and position of teacher labels on performance.

Comparison with existing methods

Compared with supervised learning when a small number of labeled data was obtained, the performance of the proposed method was shown to be superior.

Conclusion

Our 4S leverages a limited number of labeled data and a large amount of unlabeled data to extract neural regions from serial image stacks in a transductive setting. We plan to develop this method as a core module of a general-purpose annotation tool in our future work.

中文翻译：

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具有少量标记的连续电子显微镜图像的顺序半监督分割

背景

通过深度学习对电子显微连续截面图像进行分割作为一种降低使用3D重建方法进行观察的研究人员的注释成本的技术，已引起人们的关注。但是，当观察到的样本很少或扫描环境不稳定时，追求新获得的样本的泛化性能是不合适的。

新方法

我们假设一个转导设置，它仅根据部分获得的标签来预测数据集中的所有标签，同时避免追求未知数据的泛化性能。然后，我们提出了顺序半监督分割（4S），它可以从电子显微镜图像堆栈中半自动提取神经区域。该方法着眼于以下事实：相邻图像在串行图像中具有很强的相关性。我们的4S使用最少的教师标签重复训练，推理和伪标签，并对所有片段执行分割。

结果

我们使用两种类型的连续切片图像进行的实验在质量和数量方面都显示出了有效性。此外，我们通过实验澄清了教师标签的数量和位置对表现的影响。

与现有方法的比较

与仅获取少量标记数据的监督学习相比，该方法的性能优越。

结论

我们的4S利用有限数量的标记数据和大量未标记数据，以转导方式从串行图像堆栈中提取神经区域。我们计划在将来的工作中将此方法开发为通用注释工具的核心模块。