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Development of a Framework for Large Scale Three-Dimensional Pathology and Biomarker Imaging and Spatial Analytics
Yanhui Liang1, Fusheng Wang1,2, Pengyue Zhang2
1Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY.
Summary
This study introduces a scalable framework for analyzing 3D digital pathology and biomarker images, enabling detailed 3D phenotypic and molecular feature investigation for translational research.
Area of Science:
- Digital pathology
- Biomedical imaging
- Translational research
Background:
- Digital pathology offers high-throughput quantitative data extraction for research.
- Traditional 2D representations limit understanding of 3D tissue architecture and disease.
- Biomarker images contain rich information on cellular and molecular features.
Purpose of the Study:
- To develop a scalable image processing framework for quantitative 3D analysis.
- To enable investigation of 3D phenotypic and cell-specific molecular features.
- To create efficient 3D spatial data management for large-scale imaging data.
Main Methods:
- Proposed a scalable image processing framework for information-lossless 3D tissue analysis.
- Developed a generalized 3D spatial data management framework with multi-level parallelism.
- Implemented sustainable infrastructure for rapid spatial queries via efficient data processing.
Main Results:
- The framework enables quantitative investigation of 3D phenotypic and molecular features.
- Efficient processing of large-scale 3D pathology and biomarker imaging data is achieved.
- Facilitates biomedical research by providing advanced spatial data analysis capabilities.
Conclusions:
- The developed framework advances 3D digital pathology by enabling comprehensive analysis.
- It supports translational research by efficiently processing complex spatial imaging data.
- This approach enhances the understanding of disease in a 3D context.
