With the rapid advancement in large-throughput scanning technologies, digital pathology has emerged as platform with promise for diagnostic approaches, but also for high-throughput quantitative data extraction and analysis for translational research. Digital pathology and biomarker images are rich sources of information on tissue architecture, cell diversity and morphology, and molecular pathway activation. However, the understanding of disease in three-dimension (3D) has been hampered by their traditional two-dimension (2D) representations on histologic slides. In this paper, we propose a scalable image processing framework to quantitatively investigate 3D phenotypic and cell-specific molecular features from digital pathology and biomarker images in information- lossless 3D tissue space. We also develop a generalized 3D spatial data management framework with multi-level parallelism and provide a sustainable infrastructure for rapid spatial queries through scalable and efficient spatial data processing. The developed framework can facilitate biomedical research by efficiently processing large-scale, 3D pathology and in-situ biomarker imaging data.
|Original language||English (US)|
|Number of pages||10|
|Journal||AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science|
|State||Published - 2017|
- Journal Article