Abstract
Digital pathology increasingly seeks to extract quantitative vascular and microenvironmental features from routine histology images, but thin-section hematoxylin and eosin (H&E) slides can fragment vessels and obscure their spatial organization, constraining downstream computational analysis. Slide-free fluorescence-imitating brightfield imaging (FIBI) produces histology-like images directly from fresh or fixed tissue or from already prepared paraffin blocks within minutes and can better preserve apparent microvascular continuity in breast cancer and other specimens. In this exploratory digital pathology study, we acquired paired FIBI and H&E images from paraffin-embedded breast tissue blocks, manually segmented blood vessels in tumor and tumor-adjacent stroma, and quantified vascular architecture using both standard geometry-based metrics and topology-derived descriptors of inter-vessel arrangement, including a persistent-homology-based "vessel spacing" metric that captures multiscale clustering. Here, geometry refers to properties of individual vessels (size, length, and branching), whereas topology summarizes how vessels are arranged as a network, including how closely or loosely they cluster. FIBI images exhibited an easily appreciable increase in vascular information compared with matched H&E slides, with vessels appearing more continuous and more clearly resolved in both geometry (e.g., larger area, more branched, etc.) and network topology (e.g., decreased inter-vessel separation). Motivated by this qualitative impression of increased informational content, the goal of this study was to quantitatively assess and validate these differences by contrasting complementary geometry and topology-derived metrics of vessels in pairs of H&E and FIBI images. In particular, persistent homology-derived topology metrics provided information that was not linearly explained by standard geometric descriptors. Together, these findings demonstrate a practical digital pathology pipeline that integrates slide-free FIBI acquisition, vessel annotation, geometry- and topology-based feature extraction, and suggest that FIBI-derived vascular signatures, particularly when coupled with automated vessel segmentation, may provide informative input for future computational and artificial intelligence-based pathology models, and potentially clinical applications.