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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2506.02976 |
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| _version_ | 1866917149736435712 |
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| author | Zeghlache, Rachid Brahim, Ikram Conze, Pierre-Henri Lamard, Mathieu Lazouni, Mohammed El Amine Elaouaber, Zineb Aziza Lazouni, Leila Ryma Nielsen, Christopher Ahsan, Ahmad O. Wilms, Matthias Forkert, Nils D. Budimir, Lovre Antonio Matovinović, Ivana Vršnak, Donik Lončarić, Sven Zhang, Philippe Jiang, Weili Li, Yihao Hao, Yiding Frohmann, Markus Binder, Patrick Huber, Marcel Emre, Taha Araújo, Teresa Finisterra Oghbaie, Marzieh Bogunović, Hrvoje Bekkers, Amerens A. van Liebergen, Nina M. Kuijf, Hugo J. Qayyum, Abdul Mazher, Moona Niederer, Steven A. Beltrán-Carrero, Alberto J. Gómez-Valverde, Juan J. Torresano-Rodríquez, Javier Caballero-Sastre, Álvaro Carbayo, María J. Ledesma Yamagishi, Yosuke Ding, Yi Peretzke, Robin Ertl, Alexandra Fischer, Maximilian Kächele, Jessica Zehar, Sofiane Hacene, Karim Boukli Monfort, Thomas Cochener, Béatrice Daho, Mostafa El Habib Benyoussef, Anas-Alexis Quellec, Gwenolé |
| author_facet | Zeghlache, Rachid Brahim, Ikram Conze, Pierre-Henri Lamard, Mathieu Lazouni, Mohammed El Amine Elaouaber, Zineb Aziza Lazouni, Leila Ryma Nielsen, Christopher Ahsan, Ahmad O. Wilms, Matthias Forkert, Nils D. Budimir, Lovre Antonio Matovinović, Ivana Vršnak, Donik Lončarić, Sven Zhang, Philippe Jiang, Weili Li, Yihao Hao, Yiding Frohmann, Markus Binder, Patrick Huber, Marcel Emre, Taha Araújo, Teresa Finisterra Oghbaie, Marzieh Bogunović, Hrvoje Bekkers, Amerens A. van Liebergen, Nina M. Kuijf, Hugo J. Qayyum, Abdul Mazher, Moona Niederer, Steven A. Beltrán-Carrero, Alberto J. Gómez-Valverde, Juan J. Torresano-Rodríquez, Javier Caballero-Sastre, Álvaro Carbayo, María J. Ledesma Yamagishi, Yosuke Ding, Yi Peretzke, Robin Ertl, Alexandra Fischer, Maximilian Kächele, Jessica Zehar, Sofiane Hacene, Karim Boukli Monfort, Thomas Cochener, Béatrice Daho, Mostafa El Habib Benyoussef, Anas-Alexis Quellec, Gwenolé |
| contents | The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking was determined based on performance on this dataset. An auxiliary dataset from Algeria was used post-challenge to evaluate population and device shifts from submitted solutions. Two tasks were involved in the MARIO challenge. The first one was the classification of evolution between two consecutive 2D OCT B-scans. The second one was the prediction of future AMD evolution over three months for patients undergoing anti-vascular endothelial growth factor (VEGF) therapy. Thirty-five teams participated, with the top 12 finalists presenting their methods. This paper outlines the challenge's structure, tasks, data characteristics, and winning methodologies, setting a benchmark for AMD monitoring using OCT, infrared imaging, and clinical data (such as the number of visits, age, gender, etc.). The results of this challenge indicate that artificial intelligence (AI) performs as well as a physician in measuring AMD progression (Task 1) but is not yet able of predicting future evolution (Task 2). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02976 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge Zeghlache, Rachid Brahim, Ikram Conze, Pierre-Henri Lamard, Mathieu Lazouni, Mohammed El Amine Elaouaber, Zineb Aziza Lazouni, Leila Ryma Nielsen, Christopher Ahsan, Ahmad O. Wilms, Matthias Forkert, Nils D. Budimir, Lovre Antonio Matovinović, Ivana Vršnak, Donik Lončarić, Sven Zhang, Philippe Jiang, Weili Li, Yihao Hao, Yiding Frohmann, Markus Binder, Patrick Huber, Marcel Emre, Taha Araújo, Teresa Finisterra Oghbaie, Marzieh Bogunović, Hrvoje Bekkers, Amerens A. van Liebergen, Nina M. Kuijf, Hugo J. Qayyum, Abdul Mazher, Moona Niederer, Steven A. Beltrán-Carrero, Alberto J. Gómez-Valverde, Juan J. Torresano-Rodríquez, Javier Caballero-Sastre, Álvaro Carbayo, María J. Ledesma Yamagishi, Yosuke Ding, Yi Peretzke, Robin Ertl, Alexandra Fischer, Maximilian Kächele, Jessica Zehar, Sofiane Hacene, Karim Boukli Monfort, Thomas Cochener, Béatrice Daho, Mostafa El Habib Benyoussef, Anas-Alexis Quellec, Gwenolé Computer Vision and Pattern Recognition Artificial Intelligence The MARIO challenge, held at MICCAI 2024, focused on advancing the automated detection and monitoring of age-related macular degeneration (AMD) through the analysis of optical coherence tomography (OCT) images. Designed to evaluate algorithmic performance in detecting neovascular activity changes within AMD, the challenge incorporated unique multi-modal datasets. The primary dataset, sourced from Brest, France, was used by participating teams to train and test their models. The final ranking was determined based on performance on this dataset. An auxiliary dataset from Algeria was used post-challenge to evaluate population and device shifts from submitted solutions. Two tasks were involved in the MARIO challenge. The first one was the classification of evolution between two consecutive 2D OCT B-scans. The second one was the prediction of future AMD evolution over three months for patients undergoing anti-vascular endothelial growth factor (VEGF) therapy. Thirty-five teams participated, with the top 12 finalists presenting their methods. This paper outlines the challenge's structure, tasks, data characteristics, and winning methodologies, setting a benchmark for AMD monitoring using OCT, infrared imaging, and clinical data (such as the number of visits, age, gender, etc.). The results of this challenge indicate that artificial intelligence (AI) performs as well as a physician in measuring AMD progression (Task 1) but is not yet able of predicting future evolution (Task 2). |
| title | Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.02976 |