ACHIKO-D350: A dataset for early AMD detection and drusen segmentation
- Huiying Liu (Agency for Science, Technology and Research)
- Yanwu Xu (Agency for Science, Technology and Research)
- Damon W.K. Wong (Agency for Science, Technology and Research)
- Augustinus Laude (Tan Tock Seng Hospital)
- Tock Ham Lim (Tan Tock Seng Hospital)
- Jiang Liu (Agency for Science, Technology and Research)
Age related macular degeneration is the third leading cause of global blindness. Its prevalence is increasing in these years for the coming of ”aging population”. Early detection and grading can prevent it from becoming severe and protect vision. Drusen is an important indicator for AMD. Thus automatic drusen detection and segmentation has attracted much research attention in the past years. However, a barrier handicapping the research of drusen segmentation is the lack of a public dataset and test platform. To address this issue, in this paper, we publish a dataset, named ACHIKO-D350, with manually marked drusen boundary. ACHIKO-D350 includes 254 healthy fundus images and 96 fundus images with drusen. The images with drusen cover a wide range of types, including images with sparsely distributed drusen or clumped drusen, images of poor quality, and both well macular centered images and mis-centered images. ACHIKO-D350 will be used for performance evaluation of drusen segmentation methods. It will facilitate an objective evaluation and comparison.
How to Cite:
Liu, H. & Xu, Y. & Wong, D. W. & Laude, A. & Lim, T. H. & Liu, J., (2014) “ACHIKO-D350: A dataset for early AMD detection and drusen segmentation”, Ophthalmic Medical Image Analysis International Workshop 1(2014), 73-80. doi: https://doi.org/10.17077/omia.1011
Rights: Copyright © 2014, Huiying Liu, Yanwu Xu, Damon W. K. Wong, Augustinus Laude, Tock Han Lim, and Jiang Liu.
- The dataset referred to in this presentation is not available in Iowa Research Online. Please contact the authors to request access to this dataset.