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1. Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results

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, and Jakab, Andras

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

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., Comment: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648

Published
2024
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2. Biomedical image analysis competitions: The state of current participation practice

Author

Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Godau, Patrick, Cheplygina, Veronika, Kozubek, Michal, Ali, Sharib, Gupta, Anubha, Kybic, Jan, Noble, Alison, de Solórzano, Carlos Ortiz, Pachade, Samiksha, Petitjean, Caroline, Sage, Daniel, Wei, Donglai, Wilden, Elizabeth, Alapatt, Deepak, Andrearczyk, Vincent, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bawa, Vivek Singh, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Choi, Jinwook, Commowick, Olivier, Daum, Marie, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Eichhorn, Hannah, Engelhardt, Sandy, Ganz, Melanie, Girard, Gabriel, Hansen, Lasse, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Kim, Hyunjeong, Landman, Bennett, Li, Hongwei Bran, Li, Jianning, Ma, Jun, Martel, Anne, Martín-Isla, Carlos, Menze, Bjoern, Nwoye, Chinedu Innocent, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Sudre, Carole, van Wijnen, Kimberlin, Vardazaryan, Armine, Vercauteren, Tom, Wagner, Martin, Wang, Chuanbo, Yap, Moi Hoon, Yu, Zeyun, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Bao, Rina, Choi, Chanyeol, Cohen, Andrew, Dzyubachyk, Oleh, Galdran, Adrian, Gan, Tianyuan, Guo, Tianqi, Gupta, Pradyumna, Haithami, Mahmood, Ho, Edward, Jang, Ikbeom, Li, Zhili, Luo, Zhengbo, Lux, Filip, Makrogiannis, Sokratis, Müller, Dominik, Oh, Young-tack, Pang, Subeen, Pape, Constantin, Polat, Gorkem, Reed, Charlotte Rosalie, Ryu, Kanghyun, Scherr, Tim, Thambawita, Vajira, Wang, Haoyu, Wang, Xinliang, Xu, Kele, Yeh, Hung, Yeo, Doyeob, Yuan, Yixuan, Zeng, Yan, Zhao, Xin, Abbing, Julian, Adam, Jannes, Adluru, Nagesh, Agethen, Niklas, Ahmed, Salman, Khalil, Yasmina Al, Alenyà, Mireia, Alhoniemi, Esa, An, Chengyang, Anwar, Talha, Arega, Tewodros Weldebirhan, Avisdris, Netanell, Aydogan, Dogu Baran, Bai, Yingbin, Calisto, Maria Baldeon, Basaran, Berke Doga, Beetz, Marcel, Bian, Cheng, Bian, Hao, Blansit, Kevin, Bloch, Louise, Bohnsack, Robert, Bosticardo, Sara, Breen, Jack, Brudfors, Mikael, Brüngel, Raphael, Cabezas, Mariano, Cacciola, Alberto, Chen, Zhiwei, Chen, Yucong, Chen, Daniel Tianming, Cho, Minjeong, Choi, Min-Kook, Xie, Chuantao Xie Chuantao, Cobzas, Dana, Cohen-Adad, Julien, Acero, Jorge Corral, Das, Sujit Kumar, de Oliveira, Marcela, Deng, Hanqiu, Dong, Guiming, Doorenbos, Lars, Efird, Cory, Escalera, Sergio, Fan, Di, Serj, Mehdi Fatan, Fenneteau, Alexandre, Fidon, Lucas, Filipiak, Patryk, Finzel, René, Freitas, Nuno R., Friedrich, Christoph M., Fulton, Mitchell, Gaida, Finn, Galati, Francesco, Galazis, Christoforos, Gan, Chang Hee, Gao, Zheyao, Gao, Shengbo, Gazda, Matej, Gerats, Beerend, Getty, Neil, Gibicar, Adam, Gifford, Ryan, Gohil, Sajan, Grammatikopoulou, Maria, Grzech, Daniel, Güley, Orhun, Günnemann, Timo, Guo, Chunxu, Guy, Sylvain, Ha, Heonjin, Han, Luyi, Han, Il Song, Hatamizadeh, Ali, He, Tian, Heo, Jimin, Hitziger, Sebastian, Hong, SeulGi, Hong, SeungBum, Huang, Rian, Huang, Ziyan, Huellebrand, Markus, Huschauer, Stephan, Hussain, Mustaffa, Inubushi, Tomoo, Polat, Ece Isik, Jafaritadi, Mojtaba, Jeong, SeongHun, Jian, Bailiang, Jiang, Yuanhong, Jiang, Zhifan, Jin, Yueming, Joshi, Smriti, Kadkhodamohammadi, Abdolrahim, Kamraoui, Reda Abdellah, Kang, Inha, Kang, Junghwa, Karimi, Davood, Khademi, April, Khan, Muhammad Irfan, Khan, Suleiman A., Khantwal, Rishab, Kim, Kwang-Ju, Kline, Timothy, Kondo, Satoshi, Kontio, Elina, Krenzer, Adrian, Kroviakov, Artem, Kuijf, Hugo, Kumar, Satyadwyoom, La Rosa, Francesco, Lad, Abhi, Lee, Doohee, Lee, Minho, Lena, Chiara, Li, Hao, Li, Ling, Li, Xingyu, Liao, Fuyuan, Liao, KuanLun, Oliveira, Arlindo Limede, Lin, Chaonan, Lin, Shan, Linardos, Akis, Linguraru, Marius George, Liu, Han, Liu, Tao, Liu, Di, Liu, Yanling, Lourenço-Silva, João, Lu, Jingpei, Lu, Jiangshan, Luengo, Imanol, Lund, Christina B., Luu, Huan Minh, Lv, Yi, Macar, Uzay, Maechler, Leon, L., Sina Mansour, Marshall, Kenji, Mazher, Moona, McKinley, Richard, Medela, Alfonso, Meissen, Felix, Meng, Mingyuan, Miller, Dylan, Mirjahanmardi, Seyed Hossein, Mishra, Arnab, Mitha, Samir, Mohy-ud-Din, Hassan, Mok, Tony Chi Wing, Murugesan, Gowtham Krishnan, Karthik, Enamundram Naga, Nalawade, Sahil, Nalepa, Jakub, Naser, Mohamed, Nateghi, Ramin, Naveed, Hammad, Nguyen, Quang-Minh, Quoc, Cuong Nguyen, Nichyporuk, Brennan, Oliveira, Bruno, Owen, David, Pal, Jimut Bahan, Pan, Junwen, Pan, Wentao, Pang, Winnie, Park, Bogyu, Pawar, Vivek, Pawar, Kamlesh, Peven, Michael, Philipp, Lena, Pieciak, Tomasz, Plotka, Szymon, Plutat, Marcel, Pourakpour, Fattaneh, Preložnik, Domen, Punithakumar, Kumaradevan, Qayyum, Abdul, Queirós, Sandro, Rahmim, Arman, Razavi, Salar, Ren, Jintao, Rezaei, Mina, Rico, Jonathan Adam, Rieu, ZunHyan, Rink, Markus, Roth, Johannes, Ruiz-Gonzalez, Yusely, Saeed, Numan, Saha, Anindo, Salem, Mostafa, Sanchez-Matilla, Ricardo, Schilling, Kurt, Shao, Wei, Shen, Zhiqiang, Shi, Ruize, Shi, Pengcheng, Sobotka, Daniel, Soulier, Théodore, Fadida, Bella Specktor, Stoyanov, Danail, Mun, Timothy Sum Hon, Sun, Xiaowu, Tao, Rong, Thaler, Franz, Théberge, Antoine, Thielke, Felix, Torres, Helena, Wahid, Kareem A., Wang, Jiacheng, Wang, YiFei, Wang, Wei, Wang, Xiong, Wen, Jianhui, Wen, Ning, Wodzinski, Marek, Wu, Ye, Xia, Fangfang, Xiang, Tianqi, Xiaofei, Chen, Xu, Lizhan, Xue, Tingting, Yang, Yuxuan, Yang, Lin, Yao, Kai, Yao, Huifeng, Yazdani, Amirsaeed, Yip, Michael, Yoo, Hwanseung, Yousefirizi, Fereshteh, Yu, Shunkai, Yu, Lei, Zamora, Jonathan, Zeineldin, Ramy Ashraf, Zeng, Dewen, Zhang, Jianpeng, Zhang, Bokai, Zhang, Jiapeng, Zhang, Fan, Zhang, Huahong, Zhao, Zhongchen, Zhao, Zixuan, Zhao, Jiachen, Zhao, Can, Zheng, Qingshuo, Zhi, Yuheng, Zhou, Ziqi, Zou, Baosheng, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, and Maier-Hein, Lena

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Published
2022

3. Fetal Brain Tissue Annotation and Segmentation Challenge Results

Author

Payette, Kelly, Li, Hongwei, de Dumast, Priscille, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R., Oliveira, Bruno, Vilaça, João L., Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas, Aertsen, Michael, Vercauteren, Tom, Sobotka, Daniel, Langs, Georg, Alenyà, Mireia, Villanueva, Maria Inmaculada, Camara, Oscar, Fadida, Bella Specktor, Joskowicz, Leo, Weibin, Liao, Yi, Lv, Xuesong, Li, Mazher, Moona, Qayyum, Abdul, Puig, Domenec, Kebiri, Hamza, Zhang, Zelin, Xu, Xinyi, Wu, Dan, Liao, KuanLun, Wu, YiXuan, Chen, JinTai, Xu, Yunzhi, Zhao, Li, Vasung, Lana, Menze, Bjoern, Cuadra, Meritxell Bach, and Jakab, Andras

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero., Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitted

Published
2022
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6. Fetal brain tissue annotation and segmentation challenge results

Author

Payette, Kelly, Li, Hongwei Bran, de Dumast, Priscille, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R., Oliveira, Bruno, Vilaça, João L., Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas, Aertsen, Michael, Vercauteren, Tom, Sobotka, Daniel, Langs, Georg, Alenyà, Mireia, Villanueva, Maria Inmaculada, Camara, Oscar, Fadida, Bella Specktor, Joskowicz, Leo, Weibin, Liao, Yi, Lv, Xuesong, Li, Mazher, Moona, Qayyum, Abdul, Puig, Domenec, Kebiri, Hamza, Zhang, Zelin, Xu, Xinyi, Wu, Dan, Liao, Kuanlun, Wu, Yixuan, Chen, Jintai, Xu, Yunzhi, Zhao, Li, Vasung, Lana, Menze, Bjoern, Cuadra, Meritxell Bach, and Jakab, Andras

Published
2023
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11. Fetal brain tissue annotation and segmentation challenge results

Author

Payette, Kelly; https://orcid.org/0000-0001-7041-0150, Li, Hongwei Bran; https://orcid.org/0000-0002-5328-6407, de Dumast, Priscille; https://orcid.org/0000-0003-0359-9365, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R, Oliveira, Bruno, Vilaça, João L, Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas; https://orcid.org/0000-0003-1450-1634, Menze, Bjoern; https://orcid.org/0000-0003-4136-5690, Jakab, András; https://orcid.org/0000-0001-6291-9889, et al, Payette, Kelly; https://orcid.org/0000-0001-7041-0150, Li, Hongwei Bran; https://orcid.org/0000-0002-5328-6407, de Dumast, Priscille; https://orcid.org/0000-0003-0359-9365, Licandro, Roxane, Ji, Hui, Siddiquee, Md Mahfuzur Rahman, Xu, Daguang, Myronenko, Andriy, Liu, Hao, Pei, Yuchen, Wang, Lisheng, Peng, Ying, Xie, Juanying, Zhang, Huiquan, Dong, Guiming, Fu, Hao, Wang, Guotai, Rieu, ZunHyan, Kim, Donghyeon, Kim, Hyun Gi, Karimi, Davood, Gholipour, Ali, Torres, Helena R, Oliveira, Bruno, Vilaça, João L, Lin, Yang, Avisdris, Netanell, Ben-Zvi, Ori, Bashat, Dafna Ben, Fidon, Lucas; https://orcid.org/0000-0003-1450-1634, Menze, Bjoern; https://orcid.org/0000-0003-4136-5690, Jakab, András; https://orcid.org/0000-0001-6291-9889, and et al

Abstract

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Published
2023

12. Universally appliable white matter hyperintensities volumetry assisting deep-learning model for both 2D and 3D T2-fluid attenuated inversion recovery magnetic resonance imaging

Author

Rieu, ZunHyan

Subjects
Segmentation, Artificial Intelligence, Neuroradiology brain, Dementia, MR
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: The T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) method is a widely obtained sequence in magnetic resonance imaging (MRI). T2-FLAIR protocol is typically used to assess the features of various brain diseases. By observing the white...

Published
2023
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13. A novel landmark-based semi-supervised deep learning method for aneurysm detection using 3D TOF-MRA

Author

Rieu, ZunHyan

Subjects
Neuroradiology brain, Computer Applications-Detection, diagnosis, MR, Aneurysms
Abstract

Purpose Methods and materials Results Conclusion Personal information and conflict of interest References, Purpose: A brain aneurysm is a ballooning in a blood vessel in the brain. A ruptured aneurysm carries a high mortality rate, even with prompt transfer to a medical center, so It is essential to discover an aneurysm beforehand. Computed Tomography(CTDigital...

Published
2023
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16. Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI

Author

Lee, Minho, primary, Kim, JeeYoung, additional, EY Kim, Regina, additional, Kim, Hyun Gi, additional, Oh, Se Won, additional, Lee, Min Kyoung, additional, Wang, Sheng-Min, additional, Kim, Nak-Young, additional, Kang, Dong Woo, additional, Rieu, ZunHyan, additional, Yong, Jung Hyun, additional, Kim, Donghyeon, additional, and Lim, Hyun Kook, additional

Published
2020
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