1. A Supervised Joint Multi-layer Segmentation Framework for Retinal Optical Coherence Tomography Images using Conditional Random Field

    A Supervised Joint Multi-layer Segmentation Framework for Retinal Optical Coherence Tomography Images using Conditional Random Field

    Background and Objective: Accurate segmentation of the intra-retinal tissue layers in Optical Coherence Tomography (OCT) images plays an important role in the diagnosis and treatment of ocular diseases such as Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). The existing energy minimization based methods employ multiple, manually handcrafted cost terms and often fail in the presence of pathologies. In this work, we eliminate the need to handcraft the energy by learning it from training images in an end-to-end manner. Our method can be easily adapted to pathologies by re-training it on an appropriate dataset. Methods: We propose a Conditional ...

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