The performance of existing supervised neuron segmentation methods is highly dependent on the number of accurate annotations, especially when applied to large scale electron microscopy (EM) data. By extracting semantic information from unlabeled data, self-supervised methods can improve the performance of downstream tasks, among which the mask image model (MIM) has been widely used due to its simplicity and effectiveness in recovering original information from masked images. However, due to the high degree of structural locality in EM images, as well as the existence of considerable noise, many voxels contain little discriminative information, making MIM pretraining inefficient on the neuron segmentation task. To overcome this challenge, we propose a decision-based MIM that utilizes reinforcement learning (RL) to automatically search for optimal image masking ratio and masking strategy. Due to the vast exploration space, using single-agent RL for voxel prediction is impractical. Therefore, we treat each input patch as an agent with a shared behavior policy, allowing for multi-agent collaboration. Furthermore, this multi-agent model can capture dependencies between voxels, which is beneficial for the downstream segmentation task. Experiments conducted on representative EM datasets demonstrate that our approach has a significant advantage over alternative self-supervised methods on the task of neuron segmentation. Code is available at \url{https://github.com/ydchen0806/dbMiM}.

翻译：现有监督式神经元分割方法的性能高度依赖于精确标注的数量，尤其是在处理大规模电子显微镜（EM）数据时。通过从无标注数据中提取语义信息，自监督方法能够提升下游任务的性能，其中掩码图像模型（MIM）因其从掩码图像中恢复原始信息的简洁性和有效性而得到广泛应用。然而，由于EM图像中存在高度的结构局部性以及大量噪声，许多体素包含的判别信息有限，这使得MIM预训练在神经元分割任务中效率低下。为克服这一挑战，我们提出了一种基于决策的MIM方法，利用强化学习（RL）自动搜索最优的图像掩码比例和掩码策略。由于探索空间巨大，使用单智能体RL进行体素预测并不可行。因此，我们将每个输入块视为一个具有共享行为策略的智能体，从而实现多智能体协作。此外，该多智能体模型能够捕捉体素间的依赖关系，这对下游分割任务十分有利。在代表性EM数据集上的实验表明，我们的方法在神经元分割任务上相较于其他自监督方法具有显著优势。代码已开源在 \url{https://github.com/ydchen0806/dbMiM}。