Registration of retinal images is an important technique for facilitating the diagnosis and treatment of many eye diseases. Recent studies have shown that deep learning methods can be used for image registration, which is usually faster than conventional registration methods. However, it is not trivial to obtain ground truth for supervised methods and popular unsupervised methods perform not well for retinal images. Therefore, we present a weakly-supervised learning method for affine registration of fundus image. The framework consists of multiple steps, rigid registration, overlap calculation and affine registration. In addition, we introduce a keypoint matching loss to replace common similarity metrics loss used in unsupervised methods. On a fundus image dataset related to multiple eye diseases, our framework can achieve more accurate registration results than that of state-of-the-art deep learning approaches.
Retinal capillary non-perfusion (CNP) is one of diabetic retinal vascular diseases. As the capillaries are occluded, blood stops flowing to certain regions of the retina, resulting in the formation of non-perfused regions. Accurate determination of the area and change of CNP is of great significance in clinical judgment of the extent of vascular obstruction and selection of treatment methods. This paper proposes a novel generative adversarial framework, and realize the segmentation of non-perfusion regions in fundus fluorescein angiography images. The generator G of GANs is trained to produce “real” images; while an adversarially trained discriminator D is trained to do as well as possible at detecting “fakes” images from the generator. In this paper, a U-shape network is used as the discriminator. Our method is validated using on 138 clinical fundus fluorescein angiography images. Experimental results show that our method achieves more accurate segmentation results than that of state-of-the-art approaches.
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