  Spatial omics has transformed our understanding of tissue architecture by
preserving spatial context of gene expression patterns. Simultaneously,
advances in imaging AI have enabled extraction of morphological features
describing the tissue. The intersection of spatial omics and imaging AI
presents opportunities for a more holistic understanding. In this review we
introduce a framework for categorizing spatial omics-morphology combination
methods, focusing on how morphological features can be translated or integrated
into spatial omics analyses. By translation we mean finding morphological
features that spatially correlate with gene expression patterns with the
purpose of predicting gene expression. Such features can be used to generate
super-resolution gene expression maps or infer genetic information from
clinical H&E-stained samples. By integration we mean finding morphological
features that spatially complement gene expression patterns with the purpose of
enriching information. Such features can be used to define spatial domains,
especially where gene expression has preceded morphological changes and where
morphology remains after gene expression. We discuss learning strategies and
directions for further development of the field.
