Joint Localization of Optic Disc and Fovea in Ultra-widefield Fundus Images
| Autor: | Xuemin Jin, Dayong Ding, Zhuoya Yang, Xirong Li, Xixi He, Yanting Wang, Fangfang Dai |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Machine Learning in Medical Imaging ISBN: 9783030326913 MLMI@MICCAI |
| DOI: | 10.1007/978-3-030-32692-0_52 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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