Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation

Instance segmentation in electron microscopy (EM) volumes poses a significant challenge due to the complex morphology of instances and insufficient annotations. Self-supervised learning has recently emerged as a promising solution, enabling the acquisition of prior knowledge of cellular tissue structures that are essential for EM instance segmentation. However, existing pretraining methods often lack the ability to capture complex visual patterns and relationships between voxels, which results in the acquired prior knowledge being insufficient for downstream EM analysis tasks. In this paper, we propose a novel pretraining framework that leverages multiscale visual representations to capture both voxel-level and feature-level consistency in EM volumes. Specifically, our framework enforces voxel-level consistency between the outputs of a Siamese network by a reconstruction function, and incorporates a cross-attention mechanism for soft feature matching to achieve fine-grained feature-level consistency. Moreover, we propose a contrastive learning scheme on the feature pyramid to extract discriminative features across multiple scales. We extensively pretrain our method on four large-scale EM datasets, achieving promising performance improvements in representative tasks of neuron and mitochondria instance segmentation.