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Published in:
2019 | OriginalPaper | Chapter
Joint Localization of Optic Disc and Fovea in Ultra-widefield Fundus Images
Authors : Zhuoya Yang, Xirong Li, Xixi He, Dayong Ding, Yanting Wang, Fangfang Dai, Xuemin Jin
Published in: Machine Learning in Medical Imaging
Publisher: Springer International Publishing
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Abstract
Automated localization of optic disc and fovea is important for computer-aided retinal disease screening and diagnosis. Compared to previous works, this paper makes two novelties. First, we study the localization problem in the new context of ultra-widefield (UWF) fundus images, which has not been considered before. Second, we propose a spatially constrained Faster R-CNN for the task. Extensive experiments on a set of 2,182 UWF fundus images acquired from a local eye center justify the viability of the proposed model. For more than 99% of the test images, the improved Faster R-CNN localizes the fovea within one optic disc diameter to the ground truth, meanwhile detecting the optic disc with a high IoU of 0.82. The new model works reasonably well even in challenging cases where the fovea is occluded due to severe retinopathy or surgical treatments.