1. Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

    Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints

    Optical Coherence Tomography is a well established image modality in ophthalmology and used daily in the clinic. Automatic evaluation of such datasets requires an accurate segmentation of the retinal cell layers. However, due to the naturally low signal to noise ratio and the resulting bad image quality, this task remains challenging. We propose an automatic graphbased multi-surface segmentation algorithm that internally uses soft constraints to add prior information from a learned model. This improves the accuracy of the segmentation and increase the robustness to noise. Furthermore, we show that the graph size can be greatly reduced by applying a smart ...

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