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IEEE Trans Med Imaging · Jan 2010
Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs.
- Meindert Niemeijer, Bram van Ginneken, Michael J Cree, Atsushi Mizutani, Gwénolé Quellec, Clara I Sanchez, Bob Zhang, Roberto Hornero, Mathieu Lamard, Chisako Muramatsu, Xiangqian Wu, Guy Cazuguel, Jane You, Agustín Mayo, Qin Li, Yuji Hatanaka, Béatrice Cochener, Christian Roux, Fakhri Karray, María Garcia, Hiroshi Fujita, and Michael D Abramoff.
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA. meindert@isi.uu.nl
- IEEE Trans Med Imaging. 2010 Jan 1; 29 (1): 185-95.
AbstractThe detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. Abràmoff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions.
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