Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
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2024
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| author | Payette, Kelly Steger, Céline Licandro, Roxane de Dumast, Priscille Li, Hongwei Bran Barkovich, Matthew Li, Liu Dannecker, Maik Chen, Chen Ouyang, Cheng McConnell, Niccolò Miron, Alina Li, Yongmin Uus, Alena Grigorescu, Irina Gilliland, Paula Ramirez Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Wang, Haoyu Huang, Ziyan Ye, Jin Alenyà, Mireia Comte, Valentin Camara, Oscar Masson, Jean-Baptiste Nilsson, Astrid Godard, Charlotte Mazher, Moona Qayyum, Abdul Gao, Yibo Zhou, Hangqi Gao, Shangqi Fu, Jia Dong, Guiming Wang, Guotai Rieu, ZunHyan Yang, HyeonSik Lee, Minwoo Płotka, Szymon Grzeszczyk, Michal K. Sitek, Arkadiusz Daza, Luisa Vargas Usma, Santiago Arbelaez, Pablo Lu, Wenying Zhang, Wenhao Liang, Jing Valabregue, Romain Joshi, Anand A. Nayak, Krishna N. Leahy, Richard M. Wilhelmi, Luca Dändliker, Aline Ji, Hui Gennari, Antonio G. Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Kasprian, Gregor Dovjak, Gregor Rados, Milan Vasung, Lana Cuadra, Meritxell Bach Jakab, Andras |
| author_facet | Payette, Kelly Steger, Céline Licandro, Roxane de Dumast, Priscille Li, Hongwei Bran Barkovich, Matthew Li, Liu Dannecker, Maik Chen, Chen Ouyang, Cheng McConnell, Niccolò Miron, Alina Li, Yongmin Uus, Alena Grigorescu, Irina Gilliland, Paula Ramirez Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Wang, Haoyu Huang, Ziyan Ye, Jin Alenyà, Mireia Comte, Valentin Camara, Oscar Masson, Jean-Baptiste Nilsson, Astrid Godard, Charlotte Mazher, Moona Qayyum, Abdul Gao, Yibo Zhou, Hangqi Gao, Shangqi Fu, Jia Dong, Guiming Wang, Guotai Rieu, ZunHyan Yang, HyeonSik Lee, Minwoo Płotka, Szymon Grzeszczyk, Michal K. Sitek, Arkadiusz Daza, Luisa Vargas Usma, Santiago Arbelaez, Pablo Lu, Wenying Zhang, Wenhao Liang, Jing Valabregue, Romain Joshi, Anand A. Nayak, Krishna N. Leahy, Richard M. Wilhelmi, Luca Dändliker, Aline Ji, Hui Gennari, Antonio G. Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Kasprian, Gregor Dovjak, Gregor Rados, Milan Vasung, Lana Cuadra, Meritxell Bach Jakab, Andras |
| contents | Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09463 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results Payette, Kelly Steger, Céline Licandro, Roxane de Dumast, Priscille Li, Hongwei Bran Barkovich, Matthew Li, Liu Dannecker, Maik Chen, Chen Ouyang, Cheng McConnell, Niccolò Miron, Alina Li, Yongmin Uus, Alena Grigorescu, Irina Gilliland, Paula Ramirez Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Wang, Haoyu Huang, Ziyan Ye, Jin Alenyà, Mireia Comte, Valentin Camara, Oscar Masson, Jean-Baptiste Nilsson, Astrid Godard, Charlotte Mazher, Moona Qayyum, Abdul Gao, Yibo Zhou, Hangqi Gao, Shangqi Fu, Jia Dong, Guiming Wang, Guotai Rieu, ZunHyan Yang, HyeonSik Lee, Minwoo Płotka, Szymon Grzeszczyk, Michal K. Sitek, Arkadiusz Daza, Luisa Vargas Usma, Santiago Arbelaez, Pablo Lu, Wenying Zhang, Wenhao Liang, Jing Valabregue, Romain Joshi, Anand A. Nayak, Krishna N. Leahy, Richard M. Wilhelmi, Luca Dändliker, Aline Ji, Hui Gennari, Antonio G. Jakovčić, Anton Klaić, Melita Adžić, Ana Marković, Pavel Grabarić, Gracia Kasprian, Gregor Dovjak, Gregor Rados, Milan Vasung, Lana Cuadra, Meritxell Bach Jakab, Andras Image and Video Processing Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms. The resulting new methods contribute to improving the analysis of brain development in utero. |
| title | Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2402.09463 |