![]() The Development of Adaptive Optics and Its Application in Ophthalmology. Towards Accurate Segmentation of Retinal Vessels and the Optic Disc in Fundoscopic Images with Generative Adversarial Networks. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME). Weighted Res-UNet for High-Quality Retina Vessel Segmentation. Medical Image Computing and Computer Assisted Intervention- MICCAI 2018. In: Frangi A, Schnabel J, Davatzikos C, Alberola-López C, et al. Deep Supervision with Additional Labels for Retinal Vessel Segmentation Task. PixelBNN: Augmenting the Pixelcnn with Batch Normalization and the Presentation of a Fast Architecture for Retinal Vessel Segmentation. Leopold HA, Orchard J, Zelek JS, Lakshminarayanan V. Automatic quantification of superficial foveal avascular zone in optical coherence tomography angiography implemented with deep learning. Optical coherence tomography angiography of the foveal avascular zone in retinal vein occlusion. Wons J, Pfau M, Wirth MA, Freiberg FJ, et al. The precision obtained from the RV and FAZ segmentation over 316 OCT-A images from the OCT-A 500 database at 93.21% and 92.59%, where the FAZ was segmented with an accuracy of 99.83% for binary classification. We focus on two critical zones: retinal vasculature (RV) and foveal avascular zone (FAZ). ![]() In this work, we carried out multi-class image segmentation where the best characteristics are highlighted in the appropriate plexuses by comparing different neural network architectures, including U-Net, ResU-Net, and FCN. The separation and distinction of the different parts that build the macula simplify the subsequent detection of observable patterns/illnesses in the retina. Segmentation is vital in Optical Coherence Tomography Angiography (OCT-A) images. OCT-A segmentation, ResU-Net, FCN segmentation, Convolutional Neural Network Abstract Universidad Autótoma de Querétaro, México Universidad Autónoma de Querétaro, México ![]()
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