Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs
Niemeijer, Meindert; van Ginneken, Bram; Cree, Michael J.; Mizutani, Atsushi; Quellec, Gwenole; Sanchez, Clara I.; Zhang, Bob; Hornero, Roberto; Lamard, Mathieu; Muramatsu, Chisako; Wu, Xiangqian; Cazuguel, Guy; You, Jane; Mayo, Agustin; Li, Qin; Hatanaka, Yuji; Cochener, Beatrice; Roux, Christian; Karray, Fakhri; Garcia, Maria; Fujita, Hiroshi; Abramoff, Michael D.
Niemeijer, M., van Ginneken, B., Cree, M. J., Mizutani, A., Quellec, G., ... , Abramoff, M. D. (2010). Retinopathy online challenge: Automatic detection of microaneurysms in digital color fundus photographs. IEEE Transactions on Medical Imaging, 29(1), 185-195.
Permanent Research Commons link: http://hdl.handle.net/10289/3644
The 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 witheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. AbrAÂ¿moff). 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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