Context-specific image quality assessment for virtual histologic staining: checklist and guideline

Purpose: Virtual staining applies computational methods to transform optical images of biological samples into histology-like representations suitable for interpretation and analysis. Existing methods for evaluating virtual staining often prioritize metrics that do not provide a complete assessment of image quality for a given biomedical or scientific context. Approach: We review existing approaches to conduct image quality assessment (IQA) for virtual staining and identify their limitations. We make the case for context-specific IQA and propose a checklist and guideline for the comprehensive evaluation of image quality of a virtual staining system. Results: We present a context-specific IQA checklist and guideline for virtually stained images that (1) defines a specific context of use (COU), (2) explains the underlying mechanisms as they relate to COU, and (3) provides COU-specific evidence for validation. We build upon existing methods and connect IQA to the underlying imaging methodology and biological truth. Conclusions: The resulting checklist and guideline link engineering evaluation to biomedical utility and aim to enable more reliable development and evaluation of virtual staining systems.