Abstract:  Accurate and reliable image segmentation is of great importance in quantitative medical image analysis. In ophthalmology, translational applications of medical imaging were – until recently – limited to 2D analyses of fundus photographs. With a fast-growing routine clinical use of 3-D imaging modalities like optical coherence tomography (OCT), ophthalmologists (same as radiologists decades ago) are faced with ever-increasing amounts of image data to analyze. Quantitative outcomes of such analyses are growing in importance. Yet, daily interpretation of clinical ophthalmic OCT images is still typically performed visually and qualitatively, with quantitative clinical analysis being an exception rather than the norm. Since performing full OCT image segmentations in 3D is infeasible for a physician in clinical setting due to the time constraints, quantitative and highly automated analysis methods must be developed.  Our approach to simultaneous segmentation of multiple interacting surfaces appearing in the context of other interacting objects will be presented. The reported methods are part of the family of graph-based image segmentation methods dubbed LOGISMOS for Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces. This family of methods guarantees solution optimality with direct applicability to n-D problems.  The talk will present new methods and approaches developed during our ongoing ophthalmic OCT image analysis projects, including morphologic analyses of normal and pathologic retinal OCT, determination of structure—function relationships in glaucoma, methods for image-guided treatment of age-related macular degeneration, and approaches applicable to other vision impairing and/or blinding diseases.