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 https://github.com/ydchen0806/dbMiM.

提出了一种基于决策的掩蔽图像模型（drawing-based mask image model, MIM）方法，利用强化学习自动搜索最佳图像掩蔽比例和掩蔽策略，并通过多主体协作捕捉体素间的依赖关系，从而在神经元分割任务上显著优于其他自我监督方法。

多智能体强化学习的自监督神经元分割

Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement  Learning

We present a method combining affinity prediction with region agglomeration,
which improves significantly upon the state of the art of neuron segmentation
from electron microscopy (EM) in accuracy and scalability. Our method consists
of a 3D U-NET, trained to predict affinities between voxels, followed by
iterative region agglomeration. We train using a structured loss based on
MALIS, encouraging topologically correct segmentations obtained from affinity
thresholding. Our extension consists of two parts: First, we present a
quasi-linear method to compute the loss gradient, improving over the original
quadratic algorithm. Second, we compute the gradient in two separate passes to
avoid spurious gradient contributions in early training stages. Our predictions
are accurate enough that simple learning-free percentile-based agglomeration
outperforms more involved methods used earlier on inferior predictions. We
present results on three diverse EM datasets, achieving relative improvements
over previous results of 27%, 15%, and 250%. Our findings suggest that a single
method can be applied to both nearly isotropic block-face EM data and
anisotropic serial sectioned EM data. The runtime of our method scales linearly
with the size of the volume and achieves a throughput of about 2.6 seconds per
megavoxel, qualifying our method for the processing of very large datasets.

文章提出了一种结合亲和预测和区域聚合的方法，通过 3D U-NET 训练预测像素之间的亲和性，从而显著提高电子显微镜（EM）中的神经元分割准确性和可扩展性，通过实验表明，该方法在三个不同 EM 数据集上的表现相对于之前的结果提高了 27％，15％和 250％。