Self-supervised Learning of Morphological Representation for 3D EM Segments with Cluster-Instance Correlations

Abstract

Morphological analysis of various cells is essential for understanding brain functions. However, the massive data volume of electronic microscopy (EM) images brings significant challenges for cell segmentation and analysis. While obtaining sufficient data annotation for supervised deep learning methods is laborious and tedious, we propose the first self-supervised approach for learning 3D morphology representations from ultra-scale EM segments without any data annotations. Our approach, MorphConNet, leverages contrastive learning in both instance level and cluster level to enforce similarity between two augmented versions of the same segment and the compactness of representation distributions within clusters. Through experiments on the dense segmentation of the full-brain EM volume of an adult fly FAFB-FFN1, our MorphConNet shows effectiveness in learning morphological representation for accurate classification of cellular subcompartments such as somas, neurites, and glia. The self-supervised morphological representation will also facilitate other morphological analysis tasks in neuroscience.