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

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.

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.

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.

The resulting checklist and guideline link engineering evaluation to biomedical utility and aim to enable more reliable development and evaluation of virtual staining systems.