SCCS: Deep Neural Spectral Clustering for Self-Supervised Subcellular Structure Segmentation

Abstract

Subcellular structure segmentation is a fundamental task in biological imaging. Existing self-supervised representation learning combined with classical k-means clustering achieved unsupervised image segmentation, but it was constrained by time-consuming test-time pixel-wise feature extraction and clustering synchronization. This study introduces SCCS, a lightweight graph neural network-based spectral clustering framework for end-to-end subcellular structure segmentation upon superpixel graphs, greatly relieving the computational complexity in test-time numerical spectral clustering and inter-graph label inconsistency. Specifically, SCCS exploits the self-supervised masked autoencoder for representation learning and the construction of superpixel graphs (spG). Unlike per-graph scalar affinity-based spectral clustering, the proposed SCCS parameterizes the mapping from learned deep spG representations to coordinates in the spectral embedding space and the clustering assignments. The SCCS is optimized under unsupervised eigendecomposition and incremental clustering criteria, which synchronize the intra- and inter-graph spectral clustering. The proposed approach is evaluated on a publicly available volumetric electron microscopy dataset. Experiments demonstrate the effectiveness and performance gains of the proposed SCCS over the state-of-the-art in discovering a variety of subcellular structures.