Jonathan T.C. Liu

SCOPE improves AI triage in 3D pathology by using morphology-aware prototypes to identify high-risk 2D slices for pathologist review.

SCALE3D improves prostate cancer risk prediction by analyzing 3D cell interactions through supercell graphs, outperforming traditional features and reducing computational demands.

This paper introduces a 3D microdissection method combining CNC milling and light-sheet microscopy to study tumor evolution in complex 3D structures.

This study evaluates the impact of a 3D pathology workflow on DNA, RNA, and protein integrity, showing minimal effect on amplifiability and transcriptomic profiles.

The paper introduces TubuleMAP, a semi-automated 3D tubule tracking pipeline for analyzing tubular networks, achieving high throughput and accuracy in reconstructing kidney and testis tubules.

The paper uses 3D imaging to analyze human liver tissue and identifies spatial dysregulation in cirrhotic vasculobiliary architecture.

This paper develops a 3D computational pipeline called SIGHT to detect prostate cancer in pathology datasets using generative immunolabeling, achieving an F1 score comparable to pathologist agreement.

CARP3D uses deep learning to prioritize high-risk 2D sections in 3D pathology data, improving disease detection while reducing pathologist workload.

This paper introduces GIZMO, a method to create 3D grayscale hydrogel patterns for more realistic tissue engineering.