Fetal brain tissue annotation and segmentation challenge results

•Benchmark for future automatic multi-tissue fetal brain segmentation algorithms.•Used the largest publicly available fetal brain dataset with manual annotations.•U-Net is the dominant method for automatic fetal brain segmentation.•Results using the U-Net have reached a plateau.•Challenge results an...

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Vydáno v:	Medical image analysis Ročník 88; s. 102833
Hlavní autoři:	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, Jakab, Andras
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Netherlands Elsevier B.V 01.08.2023
Témata:	Algorithms Brain - diagnostic imaging Congenital disorders Female Fetal brain MRI Fetus - diagnostic imaging Head Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Multi-class image segmentation Pregnancy Super-resolution reconstructions White Matter Multi-class image segmentation Fetal brain MRI Super-resolution reconstructions Congenital disorders
ISSN:	1361-8415, 1361-8423, 1361-8423
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Abstract	•Benchmark for future automatic multi-tissue fetal brain segmentation algorithms.•Used the largest publicly available fetal brain dataset with manual annotations.•U-Net is the dominant method for automatic fetal brain segmentation.•Results using the U-Net have reached a plateau.•Challenge results analyzed from both technical and clinical perspectives. 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. [Display omitted]
AbstractList	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. •Benchmark for future automatic multi-tissue fetal brain segmentation algorithms.•Used the largest publicly available fetal brain dataset with manual annotations.•U-Net is the dominant method for automatic fetal brain segmentation.•Results using the U-Net have reached a plateau.•Challenge results analyzed from both technical and clinical perspectives. 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. [Display omitted] 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.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.
ArticleNumber	102833
Author	Joskowicz, Leo Vercauteren, Tom Langs, Georg de Dumast, Priscille Cuadra, Meritxell Bach Ben-Zvi, Ori Fidon, Lucas Mazher, Moona Pei, Yuchen Alenyà, Mireia Xuesong, Li Wang, Guotai Qayyum, Abdul Xu, Daguang Karimi, Davood Wu, Yixuan Vasung, Lana Lin, Yang Bashat, Dafna Ben Xu, Yunzhi Liao, Kuanlun Li, Hongwei Bran Licandro, Roxane Oliveira, Bruno Kebiri, Hamza Zhang, Zelin Payette, Kelly Menze, Bjoern Wang, Lisheng Chen, Jintai Gholipour, Ali Camara, Oscar Peng, Ying Puig, Domenec Villanueva, Maria Inmaculada Xu, Xinyi Vilaça, João L. Myronenko, Andriy Siddiquee, Md Mahfuzur Rahman Zhang, Huiquan Torres, Helena R. Avisdris, Netanell Dong, Guiming Sobotka, Daniel Fadida, Bella Specktor Weibin, Liao Ji, Hui Wu, Dan Liu, Hao Xie, Juanying Fu, Hao Zhao, Li Kim, Donghyeon Jakab, Andras Rieu, ZunHyan Kim, Hyun Gi Aertsen, Michael Yi, Lv
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Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States – sequence: 5 givenname: Hui surname: Ji fullname: Ji, Hui organization: Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland – sequence: 6 givenname: Md Mahfuzur Rahman surname: Siddiquee fullname: Siddiquee, Md Mahfuzur Rahman organization: Arizona State University, United States – sequence: 7 givenname: Daguang surname: Xu fullname: Xu, Daguang organization: NVIDIA, United States – sequence: 8 givenname: Andriy surname: Myronenko fullname: Myronenko, Andriy organization: NVIDIA, United States – sequence: 9 givenname: Hao surname: Liu fullname: Liu, Hao organization: Shanghai Jiaotong University, China – sequence: 10 givenname: Yuchen surname: Pei fullname: Pei, Yuchen organization: Shanghai Jiaotong University, China – sequence: 11 givenname: Lisheng surname: Wang fullname: Wang, Lisheng organization: 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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37267773$$D View this record in MEDLINE/PubMed
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Keywords	Multi-class image segmentation Fetal brain MRI Super-resolution reconstructions Congenital disorders
Language	English
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PublicationTitle	Medical image analysis
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Publisher	Elsevier B.V
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Snippet	•Benchmark for future automatic multi-tissue fetal brain segmentation algorithms.•Used the largest publicly available fetal brain dataset with manual... 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...
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SubjectTerms	Algorithms Brain - diagnostic imaging Congenital disorders Female Fetal brain MRI Fetus - diagnostic imaging Head Humans Image Processing, Computer-Assisted - methods Magnetic Resonance Imaging - methods Multi-class image segmentation Pregnancy Super-resolution reconstructions White Matter
Title	Fetal brain tissue annotation and segmentation challenge results
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