Self-Supervised Isotropic Resolution Enhancement of Expansion Microscopy via Quantized Compression

Expansion microscopy (ExM) enables nanoscale imaging for disease characterization. However, whole-organ analyses remain limited by several challenges. Current super-resolution methods either require high-resolution ground-truth data or assume spatially uniform point spread functions-assumptions that rarely hold in whole-organ imaging with depth-varying aberrations and illumination drift. Existing methods also worsen storage demands by inflating already multi-terabyte datasets without using neural compression. We propose a single-stage, self-supervised framework that addresses both resolution anisotropy and storage constraints through compression-aware isotropic super-resolution. Our approach combines a 2D lateral encoder that operates directly on raw slices to avoid memory limits with a lightweight volumetric decoder that preserves cross-slice continuity. A vector-quantized variational autoencoder (VQ-VAE) provides an information-sufficient bottleneck, achieving up to 128 × slice compression and up to 8 × axial resolution enhancement. This latent-centric design yields approximately 1000 × reduction in storage compared with storing fully isotropic volumes. We validated the proposed model on human surgical tissues and a diverse collection of biological structures across multiple imaging modalities. The framework achieves higher throughput, lower memory usage, and stronger scalability than prior methods. By designating the compressed latent space as the native storage format, it enables efficient on-demand isotropic reconstruction directly from compact representations. This combination of isotropic enhancement and neural compression framework therefore makes large-scale, whole-organ ExM analysis practical while maintaining analysis-ready accessibility, addressing a bottleneck in translating ExM to clinical biomarker discovery.