Course Organizers: Daniel Rubin and David Paik
Units: 4 units (3 units with the permission of the instructor)
Now in its third year and back by popular demand, enhanced this year with a running project and real-time audience response system!
Quarter: Spring each year
Course Website: http://bmi260.stanford.edu/
Also see Course Flyer
Beginning with the latest biological and medical imaging modalities and their applications in research and medicine, this class focuses on computational analytic and interpretive approaches to optimize extraction and use of biological and clinical imaging data for diagnostic and therapeutic translational medical applications. Topics include major image databases, fundamental methods in image processing and quantitative extraction of image features, structured recording of image information including semantic features and ontologies, indexing, search and content-based image retrieval. Case studies include linking image data to genomic, phenotypic and clinical data, developing representations of image phenotypes for use in medical decision support and research applications and the role that biomedical imaging informatics plays in new questions in biomedical science. With the permission of the instructor, students may enroll for 3 units and participate with reduced project requirements.
Brief Course Outline:
(1) overview of biological and medical imaging modalities and research/clinical applications
(2) basics of image processing and methods to extract quantitative image features
(3) semantic image features, ontologies, and structured recording of image information
(4) image indexing, search, and content based image retrieval
(5) linking image data to genome-scale data and clinical data and phenotypes
(6) representing image phenotype and using it for decision support and research application
(7) new questions in biomedicine using imaging informatics.
Case studies will also be presented. The coure will include programming projects that use images, ontologies, XML processing, and SQL databases.
Prerequisites: Programming ability at the level of CS 106A, familiarity with statistics, basic biology
Highly recommended: Knowledge of Matlab.