Learning Multiscale Consistency for Self-Supervised Electron Microscopy Instance Segmentation

Electron microscopy (EM) images are notoriously challenging to segment due to their complex structures and lack of effective annotations. Fortunately, large-scale self-supervised pretraining offers a promising solution by allowing us to acquire prior knowledge of cell and subcellular tissue structures, which can significantly improve EM instance segmentation results. However, most existing pretraining methods fail to capture the crucial local information that is essential for EM images, instead focusing only on high-level semantic information. In this paper, we propose a novel pretraining framework that leverages multiscale visual representations to adapt to the complex structures of EM images. Our framework achieves instance-level alignment by maximizing the consistency between strongly and weakly augmented images, while also incorporating a cross-attention mechanism to match multiscale features and encode more low-level information into high-level semantics. Most importantly, our approach employs multi-task optimization on the feature pyramid, enabling multiscale pixel restoration and feature comparison. We extensively pretrain our method on four large-scale EM datasets and demonstrate significant gains on neuron and mitochondria segmentation tasks. Code is available at https://github.com/ydchen0806/MS-Con-EM-Seg.