This presentation introduces 3D diagnostic perspective in routine histopathology. While 2D whole slide imaging (WSI) focuses on pixel-based analysis of selected regions of interest (ROIs), 3D whole block imaging (WBI) captures the full tissue volume, moving from pixels to voxels and from ROIs to volumes of interest (VOIs).
By imaging entire formalin-fixed paraffin-embedded (FFPE) blocks, WBI allows for deeper tissue evaluation, offering structural insights beyond the plane of section. This enhances diagnostic accuracy by visualizing spatial relationships that may be lost in 2D slices. The approach enables more comprehensive examination and potentially improves artificial intelligence applications by providing richer, volumetric data. This may lead to better disease modeling, more informed clinical decisions, and reduced sampling bias.
Overall, integrating 3D block-level imaging into routine practice may transform how pathologists interpret complex cases, especially by revealing hidden patterns and structures not accessible through conventional slide-based workflows.
Selim Sevim, M.D., is a pathologist with a background in digital pathology, artificial intelligence, and 3D tissue imaging. After completing his pathology residency at Ankara University, he joined the Cancer Early Detection Advanced Research Center (CEDAR) at OHSU. His work focuses on AI-assisted histopathological analysis and novel computational tools for cancer diagnosis.
