Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Wang, Kang Qin, Chen Shi, Zhang Wang, Haoran Zhang, Xiwen Chen, Chen Ouyang, Cheng Dai, Chengliang Mo, Yuanhan Dai, Chenchen Kuang, Xutong Li, Ruizhe Chen, Xin Yue, Xiuzheng Tian, Song Mora-Rubio, Alejandro Punithakumar, Kumaradevan Gong, Shizhan Dou, Qi Amirrajab, Sina Khalil, Yasmina Al Scannell, Cian M. Fan, Lexiaozi Yang, Huili Sun, Xiaowu van der Geest, Rob Arega, Tewodros Weldebirhan Meriaudeau, Fabrice Özer, Caner Ranem, Amin Kalkhof, John Öksüz, İlkay Mukhopadhyay, Anirban Qayyum, Abdul Mazher, Moona Niederer, Steven A Garcia-Cabrera, Carles Arazo, Eric Grzeszczyk, Michal K. Płotka, Szymon Ma, Wanqin Li, Xiaomeng Ge, Rongjun Kou, Yongqing Chen, Xinrong Wang, He Wang, Chengyan Bai, Wenjia Wang, Shuo |
| author_facet | Wang, Kang Qin, Chen Shi, Zhang Wang, Haoran Zhang, Xiwen Chen, Chen Ouyang, Cheng Dai, Chengliang Mo, Yuanhan Dai, Chenchen Kuang, Xutong Li, Ruizhe Chen, Xin Yue, Xiuzheng Tian, Song Mora-Rubio, Alejandro Punithakumar, Kumaradevan Gong, Shizhan Dou, Qi Amirrajab, Sina Khalil, Yasmina Al Scannell, Cian M. Fan, Lexiaozi Yang, Huili Sun, Xiaowu van der Geest, Rob Arega, Tewodros Weldebirhan Meriaudeau, Fabrice Özer, Caner Ranem, Amin Kalkhof, John Öksüz, İlkay Mukhopadhyay, Anirban Qayyum, Abdul Mazher, Moona Niederer, Steven A Garcia-Cabrera, Carles Arazo, Eric Grzeszczyk, Michal K. Płotka, Szymon Ma, Wanqin Li, Xiaomeng Ge, Rongjun Kou, Yongqing Chen, Xinrong Wang, He Wang, Chengyan Bai, Wenjia Wang, Shuo |
| contents | Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In clinical practice, CMR acquisitions are frequently degraded by respiratory motion, yet the robustness of deep learning models against such artifacts remains an underexplored problem. To promote research in this domain, we organized the MICCAI CMRxMotion challenge. We curated and publicly released a dataset of 320 CMR cine series from 40 healthy volunteers who performed specific breathing protocols to induce a controlled spectrum of motion artifacts. The challenge comprised two tasks: 1) automated image quality assessment to classify images based on motion severity, and 2) robust myocardial segmentation in the presence of motion artifacts. A total of 22 algorithms were submitted and evaluated on the two designated tasks. This paper presents a comprehensive overview of the challenge design and dataset, reports the evaluation results for the top-performing methods, and further investigates the impact of motion artifacts on five clinically relevant biomarkers. All resources and code are publicly available at: https://github.com/CMRxMotion |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge Wang, Kang Qin, Chen Shi, Zhang Wang, Haoran Zhang, Xiwen Chen, Chen Ouyang, Cheng Dai, Chengliang Mo, Yuanhan Dai, Chenchen Kuang, Xutong Li, Ruizhe Chen, Xin Yue, Xiuzheng Tian, Song Mora-Rubio, Alejandro Punithakumar, Kumaradevan Gong, Shizhan Dou, Qi Amirrajab, Sina Khalil, Yasmina Al Scannell, Cian M. Fan, Lexiaozi Yang, Huili Sun, Xiaowu van der Geest, Rob Arega, Tewodros Weldebirhan Meriaudeau, Fabrice Özer, Caner Ranem, Amin Kalkhof, John Öksüz, İlkay Mukhopadhyay, Anirban Qayyum, Abdul Mazher, Moona Niederer, Steven A Garcia-Cabrera, Carles Arazo, Eric Grzeszczyk, Michal K. Płotka, Szymon Ma, Wanqin Li, Xiaomeng Ge, Rongjun Kou, Yongqing Chen, Xinrong Wang, He Wang, Chengyan Bai, Wenjia Wang, Shuo Image and Video Processing Computer Vision and Pattern Recognition Deep learning models have achieved state-of-the-art performance in automated Cardiac Magnetic Resonance (CMR) analysis. However, the efficacy of these models is highly dependent on the availability of high-quality, artifact-free images. In clinical practice, CMR acquisitions are frequently degraded by respiratory motion, yet the robustness of deep learning models against such artifacts remains an underexplored problem. To promote research in this domain, we organized the MICCAI CMRxMotion challenge. We curated and publicly released a dataset of 320 CMR cine series from 40 healthy volunteers who performed specific breathing protocols to induce a controlled spectrum of motion artifacts. The challenge comprised two tasks: 1) automated image quality assessment to classify images based on motion severity, and 2) robust myocardial segmentation in the presence of motion artifacts. A total of 22 algorithms were submitted and evaluated on the two designated tasks. This paper presents a comprehensive overview of the challenge design and dataset, reports the evaluation results for the top-performing methods, and further investigates the impact of motion artifacts on five clinically relevant biomarkers. All resources and code are publicly available at: https://github.com/CMRxMotion |
| title | Extreme Cardiac MRI Analysis under Respiratory Motion: Results of the CMRxMotion Challenge |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.19165 |