ACCREDIT: A Quality-Aware Agentic Engine for Cell-resolved Cross-modal Image Registration with Dynamic Iterative Tuning

Abstract

Spatial omics across complementary modalities is transforming our understanding of tissue architecture. Realizing this potential requires accurate and robust registration of cross-platform molecular images with hematoxylin-and-eosin (H&E) sections, the primary morphological reference for pathology. Existing methods, however, often fail silently when image orientation is unknown, image contrast is inverted, or tissue overlap is incomplete, producing erroneous registrations without alerting users or attempting recovery. Here, we present ACCREDIT, a quality-aware agentic framework that redefines cross-modal registration as an adaptive decision-making process rather than a one-shot computation. ACCREDIT combines deterministic registration pipelines with a reference-free composite quality score that automatically evaluates registration quality and rejects plausible but biologically incorrect registrations. When registration quality is insufficient, a large language model (LLM)-based rescue agent autonomously diagnoses failure modes and selects targeted recovery strategies, while an optional strategy-learning module captures expert-validated corrections for future reuse. Across Xenium, CODEX, cell-boundary, and IHC-to-H&E registration tasks, ACCREDIT outperformed competing methods by detecting registration failures and improving alignment quality through automated recovery and rescue. Ultimately, ACCREDIT enables robust integration of histology and spatial molecular profiling, providing a foundation for translating spatial omics into routine H&E-based pathology workflows.