1. LF-UNet - A Novel Anatomical-Aware Dual-Branch Cascaded Deep Neural Network for Segmentation of Retinal Layers and Fluid from Optical Coherence Tomography Images

    LF-UNet - A Novel Anatomical-Aware Dual-Branch Cascaded Deep Neural Network for Segmentation of Retinal Layers and Fluid from Optical Coherence Tomography Images

    Computer-assistant diagnosis of retinal disease relies heavily on the accurate detection of retinal boundaries and other pathological features such as fluid accumulation. Optical coherence tomography (OCT) is a non-invasive ophthalmological imaging technique that has become a standard modality in the field due to its ability to detect cross-sectional retinal pathologies at the micrometer level. In this work, we presented a novel framework to achieve simultaneous retinal layers and fluid segmentation. A dual-branch deep neural network, termed LF-UNet, was proposed which combines the expansion path of the U-Net and original fully convolutional network, with a dilated network. In addition, we introduced ...

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