CASC: Content-Aware Compaction of Sparse Microscopy Images

Abstract

Microscopy datasets are often spatially sparse, wherein relevant structures occupy only a small fraction of the total field of view (FOV), leaving large regions of background devoid of signal. This inherent inefficiency creates file sizes that are larger than needed, which increases the time needed for computational image data analysis and processing, and means unnecessarily large data volumes. In this work, a classical open-source method for content-aware spatial compaction of microscopy images (CASC) is reported that explicitly removes spatial redundancy by reorganising foreground objects into a new, smaller image. CASC combines adaptive intensity normalisation, statistical thresholding, morphological refinement, and connected-component analysis to isolate foreground structures. These structures are then extracted with contextual padding and repacked into a compact domain using a heuristic shelf-based spatial packing strategy. CASC intentionally destroys the spatial topology of the image but preserves pixel intensities exactly, retaining object-level information. The method achieves demonstrable reductions in image area and background content while maintaining high object-level preservation of biological structures, with a reduction in file size of more than 390-fold shown in real image datasets.