Professor Jon Liu’s lab is developing non-destructive slide-free 3D pathology methods for clinical decision support and surgical guidance, as well as for biological investigations. In comparison to conventional slide-based pathology, 3D pathology provides: (1) vastly greater sampling of tissue specimens including whole biopsies and surgical margins, (2) volumetric imaging of cell distributions and 3D tissue structures that are prognostic and predictive, and (3) a non-destructive and reversible workflow that preserves valuable specimens for downstream molecular assays.
Due to the immense size of feature-rich 3D pathology datasets, new challenges exist in terms of data management, human visualization, and computer-aided interpretation. We have been working on a full stack of technologies to facilitate the pre-clinical and clinical adoption of 3D pathology, from sample preparation (e.g. reversible optical clearing and fluorescence labeling), high-throughput imaging with open-top light-sheet (OTLS) microscopes developed in our lab, to data processing and AI-based image triage and analysis. For AI analyses, we are developing both traditional machine classifiers based on intuitive “hand-crafted” 3D features, and deep-learning classifiers based on sub-visual 3D features.
Our non-destructive large-volume digital pathology methods are synergistic with the growing fields of radiomics and genomics, which collectively have the potential to improve treatment decisions for diverse patient populations.
