Abstract
From molecules to organelles, cells exhibit recurring structural motifs across multiple scales. Understanding these structures provides insights into their functional roles. While superresolution microscopy can visualise such patterns, manual detection in large datasets is challenging and biased. We present the Structural Repetition Detector (SReD), an unsupervised computational framework that identifies repetitive biological structures by exploiting local texture repetition. SReD formulates structure detection as a similarity-matching problem between local image regions. It detects recurring patterns without prior knowledge or constraints on the imaging modality. We demonstrate SReD's capabilities on various fluorescence microscopy images. Quantitative analyses of three datasets highlight SReD's utility: estimating the periodicity of spectrin rings in neurons, detecting HIV-1 viral assembly, and evaluating microtubule dynamics modulated by EB3. Our open-source ImageJ and Fiji plugin enables unbiased analysis of repetitive structures across imaging modalities in diverse biological contexts.