crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023
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2025
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| author | Wijethilake, Navodini Dorent, Reuben Ivory, Marina Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Okasha, Mohamed Oviedova, Anna Dong, Hexin Kang, Bogyeong Sallé, Guillaume Han, Luyi Zhao, Ziyuan Liu, Han Fan, Yubo Yang, Tao Hardan, Shahad Alasmawi, Hussain Sanjeev, Santosh Zhuang, Yuzhou Kondo, Satoshi Calisto, Maria Baldeon Noman, Shaikh Muhammad Uzair Chen, Cancan Oguz, Ipek Zhang, Rongguo Rezaei, Mina Lai-Yuen, Susana K. Kasai, Satoshi Huang, Yunzhi Hung, Chih-Cheng Yaqub, Mohammad Wang, Lisheng Dawant, Benoit M. Guan, Cuntai Mann, Ritse Jaouen, Vincent Kam, Tae-Eui Zhang, Li Shapey, Jonathan Vercauteren, Tom |
| author_facet | Wijethilake, Navodini Dorent, Reuben Ivory, Marina Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Okasha, Mohamed Oviedova, Anna Dong, Hexin Kang, Bogyeong Sallé, Guillaume Han, Luyi Zhao, Ziyuan Liu, Han Fan, Yubo Yang, Tao Hardan, Shahad Alasmawi, Hussain Sanjeev, Santosh Zhuang, Yuzhou Kondo, Satoshi Calisto, Maria Baldeon Noman, Shaikh Muhammad Uzair Chen, Cancan Oguz, Ipek Zhang, Rongguo Rezaei, Mina Lai-Yuen, Susana K. Kasai, Satoshi Huang, Yunzhi Hung, Chih-Cheng Yaqub, Mohammad Wang, Lisheng Dawant, Benoit M. Guan, Cuntai Mann, Ritse Jaouen, Vincent Kam, Tae-Eui Zhang, Li Shapey, Jonathan Vercauteren, Tom |
| contents | The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Vestibular Schwannoma (VS) and cochlea segmentation on T2 scans for more cost-effective VS management. Over time, the challenge objectives have evolved to enhance its clinical relevance. The challenge evolved from using single-institutional data and basic segmentation in 2021 to incorporating multi-institutional data and Koos grading in 2022, and by 2023, it included heterogeneous routine data and sub-segmentation of intra- and extra-meatal tumour components. In this work, we report the findings of the 2022 and 2023 editions and perform a retrospective analysis of the challenge progression over the years. The observations from the successive challenge contributions indicate that the number of outliers decreases with an expanding dataset. This is notable since the diversity of scanning protocols of the datasets concurrently increased. The winning approach of the 2023 edition reduced the number of outliers on the 2021 and 2022 testing data, demonstrating how increased data heterogeneity can enhance segmentation performance even on homogeneous data. However, the cochlea Dice score declined in 2023, likely due to the added complexity from tumour sub-annotations affecting overall segmentation performance. While progress is still needed for clinically acceptable VS segmentation, the plateauing performance suggests that a more challenging cross-modal task may better serve future benchmarking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12006 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023 Wijethilake, Navodini Dorent, Reuben Ivory, Marina Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Okasha, Mohamed Oviedova, Anna Dong, Hexin Kang, Bogyeong Sallé, Guillaume Han, Luyi Zhao, Ziyuan Liu, Han Fan, Yubo Yang, Tao Hardan, Shahad Alasmawi, Hussain Sanjeev, Santosh Zhuang, Yuzhou Kondo, Satoshi Calisto, Maria Baldeon Noman, Shaikh Muhammad Uzair Chen, Cancan Oguz, Ipek Zhang, Rongguo Rezaei, Mina Lai-Yuen, Susana K. Kasai, Satoshi Huang, Yunzhi Hung, Chih-Cheng Yaqub, Mohammad Wang, Lisheng Dawant, Benoit M. Guan, Cuntai Mann, Ritse Jaouen, Vincent Kam, Tae-Eui Zhang, Li Shapey, Jonathan Vercauteren, Tom Image and Video Processing Computer Vision and Pattern Recognition The cross-Modality Domain Adaptation (crossMoDA) challenge series, initiated in 2021 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), focuses on unsupervised cross-modality segmentation, learning from contrast-enhanced T1 (ceT1) and transferring to T2 MRI. The task is an extreme example of domain shift chosen to serve as a meaningful and illustrative benchmark. From a clinical application perspective, it aims to automate Vestibular Schwannoma (VS) and cochlea segmentation on T2 scans for more cost-effective VS management. Over time, the challenge objectives have evolved to enhance its clinical relevance. The challenge evolved from using single-institutional data and basic segmentation in 2021 to incorporating multi-institutional data and Koos grading in 2022, and by 2023, it included heterogeneous routine data and sub-segmentation of intra- and extra-meatal tumour components. In this work, we report the findings of the 2022 and 2023 editions and perform a retrospective analysis of the challenge progression over the years. The observations from the successive challenge contributions indicate that the number of outliers decreases with an expanding dataset. This is notable since the diversity of scanning protocols of the datasets concurrently increased. The winning approach of the 2023 edition reduced the number of outliers on the 2021 and 2022 testing data, demonstrating how increased data heterogeneity can enhance segmentation performance even on homogeneous data. However, the cochlea Dice score declined in 2023, likely due to the added complexity from tumour sub-annotations affecting overall segmentation performance. While progress is still needed for clinically acceptable VS segmentation, the plateauing performance suggests that a more challenging cross-modal task may better serve future benchmarking. |
| title | crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023 |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12006 |