Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs
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...
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| Vydané v: | IEEE transactions on medical imaging Ročník 29; číslo 1; s. 185 - 195 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
IEEE
01.01.2010
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
| Predmet: | |
| ISSN: | 0278-0062, 1558-254X, 1558-254X |
| On-line prístup: | Získať plný text |
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| Abstract | 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 withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. AbrA¿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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| AbstractList | 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. AbrAmoff). 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. 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 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. 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 were 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 (MN, BVG and MDA). 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. 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 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.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 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. 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. 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. 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 withheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. AbrA¿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. |
| Author | You, Jane Cochener, Beatrice Cree, Michael J Quellec, Gwenole Mayo, AgustIn Karray, Fakhri Zhang, Bob Abramoff, Michael D Mizutani, Atsushi Muramatsu, Chisako Sanchez, Clara I Lamard, Mathieu Wu, Xiangqian Qin Li Hatanaka, Yuji Fujita, Hiroshi Niemeijer, Meindert Hornero, Roberto Roux, Christian van Ginneken, Bram Cazuguel, Guy Garcia, MarIa |
| Author_xml | – sequence: 1 givenname: Meindert surname: Niemeijer fullname: Niemeijer, Meindert email: meindert@isi.uu.nl organization: Univ. of Bretagne Occidentale, Brest, France – sequence: 2 givenname: Bram surname: van Ginneken fullname: van Ginneken, Bram organization: Image Sci. Inst., Utrecht, Netherlands – sequence: 3 givenname: Michael J surname: Cree fullname: Cree, Michael J organization: Dept. of Eng., Univ. of Waikato, Hamilton, New Zealand – sequence: 4 givenname: Atsushi surname: Mizutani fullname: Mizutani, Atsushi organization: Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan – sequence: 5 givenname: Gwenole surname: Quellec fullname: Quellec, Gwenole organization: Inst. Telecom, Brest, France – sequence: 6 givenname: Clara I surname: Sanchez fullname: Sanchez, Clara I organization: Biomed. Eng. Group (GIB), Univ. of Valladolid, Valladolid, Spain – sequence: 7 givenname: Bob surname: Zhang fullname: Zhang, Bob organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada – sequence: 8 givenname: Roberto surname: Hornero fullname: Hornero, Roberto organization: Biomed. Eng. Group (GIB), Univ. of Valladolid, Valladolid, Spain – sequence: 9 givenname: Mathieu surname: Lamard fullname: Lamard, Mathieu organization: Univ. of Bretagne Occidentale, Brest, France – sequence: 10 givenname: Chisako surname: Muramatsu fullname: Muramatsu, Chisako organization: Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan – sequence: 11 givenname: Xiangqian surname: Wu fullname: Wu, Xiangqian organization: Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China – sequence: 12 givenname: Guy surname: Cazuguel fullname: Cazuguel, Guy organization: Inst. Telecom, Brest, France – sequence: 13 givenname: Jane surname: You fullname: You, Jane organization: Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China – sequence: 14 givenname: AgustIn surname: Mayo fullname: Mayo, AgustIn organization: Dept. of Stat. & Operative Investig., Univ. of Valladolid, Valladolid, Spain – sequence: 15 surname: Qin Li fullname: Qin Li organization: Dept. of Comput., Hong Kong Polytech. Univ., Hong Kong, China – sequence: 16 givenname: Yuji surname: Hatanaka fullname: Hatanaka, Yuji organization: Dept. of Electron. Syst. Eng., Univ. of Shiga Prefecture, Hikone, Japan – sequence: 17 givenname: Beatrice surname: Cochener fullname: Cochener, Beatrice organization: Univ. of Bretagne Occidentale, Brest, France – sequence: 18 givenname: Christian surname: Roux fullname: Roux, Christian organization: Inst. Telecom, Brest, France – sequence: 19 givenname: Fakhri surname: Karray fullname: Karray, Fakhri organization: Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada – sequence: 20 givenname: MarIa surname: Garcia fullname: Garcia, MarIa organization: Biomed. Eng. Group (GIB), Univ. of Valladolid, Valladolid, Spain – sequence: 21 givenname: Hiroshi surname: Fujita fullname: Fujita, Hiroshi organization: Dept. of Intell. Image Inf., Gifu Univ., Gifu, Japan – sequence: 22 givenname: Michael D surname: Abramoff fullname: Abramoff, Michael D organization: Dept. of Ophthalmology & Visual Sci., Univ. of Iowa Hosp. & Clinics, Iowa City, IA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19822469$$D View this record in MEDLINE/PubMed https://hal.science/hal-00473901$$DView record in HAL |
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| SubjectTerms | Algorithms Aneurysm - diagnosis Bayes Theorem Biomedical engineering Biomedical imaging Blindness Cities and towns Competition Computer aided detection computer aided diagnosis Computer Science Databases, Factual Diabetes diabetic retinopathy Diagnostic Techniques, Ophthalmological Engineering Sciences False Positive Reactions Fundus Oculi fundus photographs Human Humans On-line systems Online Photography - methods retina Retinal Diseases - diagnosis Retinal Vessels - pathology Retinopathy Retinopathy Online Challenge (ROC) competition ROC Curve Signal and Image processing Statistics Systems engineering and theory Telecommunications USA Councils Websites |
| Title | Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs |
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