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Updated: May 2, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Optic disc and cup segmentation from color fundus photograph using graph cut with priors
Yuanjie Zheng1, Dwight Stambolian2, Joan O'Brien2
1Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Abstract:
For automatic segmentation of optic disc and cup from color fundus photograph, we describe a fairly general energy function that can naturally fit into a global optimization framework with graph cut. Distinguished from most previous work, our energy function includes priors on the shape & location of disc & cup, the rim thickness and the geometric interaction of "disc contains cup". These priors together with the effective optimization of graph cut enable our algorithm to generate reliable and robust solutions. Our approach is able to outperform several state-of-the-art segmentation methods, as shown by a set of experimental comparisons with manual delineations and a series of results of correlations with the assessments of a merchant-provided software from Optical Coherence Tomography (OCT) regarding several cup and disc parameters.

