Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement
  Learning
Yinda Chen Wei Huang Shenglong Zhou Qi Chen Zhiwei Xiong
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}.