About ci 2 at UCSF

Four Key Areas

Translation to Clinical Practice1) Translation to Clinical Practice

By providing imaging databases, annotation models, and visualization tools, we can nimbly implement and evaluate new techniques for Radiologists and for referring clinical services.

Enterprise Integration2) Enterprise Integration

Offering domain expertise to integrate imaging across the enterprise, we can deliver ready access to clinical data for big data analytics in epidemiology and population health studies.

Translation to Clinical Practice3) Quantitative Imaging

Quantitative Analysis is key to improved diagnosis and prediction. Tools under development include organ and tissue segmentation, automated volumetry and morphological quantification, and disease visualization. Together these tools provide a computational "cockpit" for objective disease characterization to expedite diagnosis, improve accuracy and enhance therapeutic decisions. Using time series of images also enables modeling of natural history, disease progression, and therapeutic response to optimize patient care.

Enterprise Integration4) Robust Image Acquisition & Analysis

There are countless sources of variability in medical images. One major goal of the Center is to improve the value of Radiology by reducing systemic variability. By standardizing acquisition for each patient, it will be possible to drive down this variability and achieve faster, safer, and higher-quality images in patients undergoing imaging examinations. The ability to robustly acquire data, reduce noise, tailor parameters of exams for patients, and derive quantitative metrics from imaging data are essential to achieve consistent patient outcomes.

Five Pillars


Serves as the conduit for bringing transformative digital image analysis to the Radiology and Biomedical Imaging clinic and links closely with Radiology and Biomedical Imaging PACS operations and together provide UCSF Health unique quantitate image analytics and metrics to impact patient outcome.


Provides core competence to facilitate and coordinate data and data science resources to advice intelligent imaging programs that span the RBI research computing infrastructure (links with SCS), campus resources (BCHSI, Information Commons, Wynton, CDHI) and clinical endpoints in RBI (PACS) and campus (EHR).


Is largely academic and is the foundation for getting federal and other funding, supporting the research of students, post-doctoral researchers, faculty and collaborators, disseminating the science via publications, and ensuring presence at meetings and conferences.



Develops and provides research training opportunities in artificial intelligence applications in biomedical imaging, linking with radiological reports, other meta data, and disease trajectory modeling across all levels including high school, undergraduate, graduate, post-graduate, residents and medical students.


Develops, manages and implements ci2’s communications and marketing strategies.