Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
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 segmentat...
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| Veröffentlicht in: | IEEE transactions on medical imaging Jg. 44; H. 3; S. 1257 - 1272 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
01.03.2025
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| ISSN: | 0278-0062, 1558-254X, 1558-254X |
| Online-Zugang: | Volltext |
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| 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, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused 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 centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average <inline-formula> <tex-math notation="LaTeX">95^{\text {th}} </tex-math></inline-formula> percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. 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. |
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| AbstractList | 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, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused 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 centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average <inline-formula> <tex-math notation="LaTeX">95^{\text {th}} </tex-math></inline-formula> percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. 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. 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, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused 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 centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. 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. 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, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused 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 centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. 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.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, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused 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 centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. 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. |
| Author | Grabaric, Gracia Ouyang, Cheng Alenya, Mireia Li, Liu Usma, Santiago McConnell, Niccolo Cuadra, Meritxell Bach Dumast, Priscille de Masson, Jean-Baptiste Markovic, Pavel Zhou, Hangqi Mazher, Moona Dannecker, Maik Ye, Jin Chen, Chen Joshi, Anand A. Wang, Guotai Qayyum, Abdul Xu, Daguang Vasung, Lana Jakovcic, Anton Rados, Milan Li, Hongwei Bran Licandro, Roxane Yang, HyeonSik Godard, Charlotte Valabregue, Romain Leahy, Richard M. Payette, Kelly Camara, Oscar Lu, Wenying Dandliker, Aline Li, Yongmin Huang, Ziyan Zhang, Wenhao Nayak, Krishna N. Lee, Minwoo Grzeszczyk, Michal K. Gao, Shangqi Myronenko, Andriy Comte, Valentin Wang, Haoyu Gao, Yibo Klaic, Melita Siddiquee, Md Mahfuzur Rahman Fu, Jia Sitek, Arkadiusz Dong, Guiming Daza, Luisa Vargas Dovjak, Gregor Miron, Alina Gilliland, Paula Ramirez Arbelaez, Pablo Steger, Celine Nilsson, Astrid Ji, Hui Uus, Alena Grigorescu, Irina Adzic, Ana Jakab, Andras Rieu, ZunHyan Kasprian, Gregor Plotka, Szymon Wilhelmi, Luca Liang, Jing Barkovich, Matthew Gennari, Antonio G. |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39475746$$D View this record in MEDLINE/PubMed |
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| References_xml | – ident: ref10 doi: 10.1109/JBHI.2023.3267857 – year: 2020 ident: ref19 article-title: Meribach/mevislabFetalMRI: MEVISLAB MIAL super-resolution reconstruction of fetal brain MRI v1.0 – ident: ref43 doi: 10.5555/2946645.2946704 – ident: ref48 doi: 10.1007/978-3-031-45544-5_1 – ident: ref12 doi: 10.1016/j.media.2020.101796 – ident: ref26 doi: 10.1038/s41598-017-00525-w – year: 2022 ident: ref17 article-title: Fetal tissue annotation challenge – ident: ref49 doi: 10.1007/978-3-030-60334-2_29 – ident: ref50 doi: 10.1016/j.media.2022.102731 – ident: ref41 doi: 10.1109/ICDMW.2016.0041 – ident: ref15 doi: 10.1038/s41598-022-10335-4 – ident: ref36 doi: 10.1038/s41592-020-01008-z – ident: ref24 doi: 10.1007/978-3-319-46475-6_25 – ident: ref30 doi: 10.1007/978-3-540-33259-6_7 – ident: ref27 doi: 10.1186/s12880-015-0068-x – volume: 309 issue: 1 year: 2023 ident: ref46 article-title: Reliability and feasibility of low-field-strength fetal MRI at 0.55 T during pregnancy publication-title: Radiology – ident: ref4 doi: 10.1371/journal.pmed.1002683 – ident: ref33 doi: 10.3389/fnins.2020.591683 – ident: ref20 doi: 10.1016/j.media.2012.07.004 – ident: ref1 doi: 10.1002/cmr.a.21321 – ident: ref44 doi: 10.3389/fnins.2022.886772 – volume: 121 start-page: 241 volume-title: Proc. 3rd Conf. Med. Imag. Deep Learn. ident: ref32 article-title: A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling – ident: ref22 doi: 10.1093/cercor/bhab066 – ident: ref9 doi: 10.1148/ryai.2021210097 – ident: ref11 doi: 10.1016/j.media.2023.102833 – ident: ref13 doi: 10.1038/s41597-021-00946-3 – ident: ref7 doi: 10.1109/TMI.2021.3055428 – ident: ref5 doi: 10.1109/TBME.2021.3117407 – ident: ref39 doi: 10.1007/978-3-031-16443-9_15 – ident: ref34 doi: 10.1109/ISBI.2011.5872814 – year: 2019 ident: ref3 article-title: Machine learning with multi-site imaging data: An empirical study on the impact of scanner effects publication-title: arXiv:1910.04597 – start-page: 83 volume-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops ident: ref42 article-title: Style augmentation: Data augmentation via style randomization – ident: ref38 doi: 10.1007/978-3-030-59710-8_65 – ident: ref6 doi: 10.1109/TMI.2021.3090082 – ident: ref18 doi: 10.1016/j.neuroimage.2015.06.018 – ident: ref47 doi: 10.1007/978-3-031-43990-2_34 – ident: ref31 doi: 10.1109/TMI.2020.3046579 – ident: ref35 doi: 10.1007/978-3-030-87735-4_19 – ident: ref40 doi: 10.1007/978-3-030-58545-7_18 – year: 2024 ident: ref16 article-title: Supplementary information for the fetal tissue annotation 2022 challenge results – ident: ref29 doi: 10.1038/s41598-021-82017-6 – ident: ref21 doi: 10.1007/978-1-4614-7657-3_19 – ident: ref28 doi: 10.1016/j.media.2023.102833 – ident: ref14 doi: 10.1007/978-3-030-00937-3_45 – ident: ref8 doi: 10.3389/fninf.2021.805669 – ident: ref2 doi: 10.1148/ryai.2020190195 – volume-title: MONAI: Medical Open Network for AI year: 2020 ident: ref37 – ident: ref25 doi: 10.1016/j.neuroimage.2019.116324 – ident: ref23 doi: 10.1109/TMI.2007.906087 – ident: ref45 doi: 10.12688/openreseurope.13914.2 |
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| Snippet | Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past... |
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| SubjectTerms | Algorithms Biomedical imaging Brain - diagnostic imaging Brain - embryology Deep learning domain generalization Female fetal brain MRI Fetus - diagnostic imaging Hospitals Humans Image Processing, Computer-Assisted - methods Image segmentation Imaging Magnetic resonance imaging Magnetic Resonance Imaging - methods Measurement multi-class image segmentation Pregnancy Superresolution Testing Topology Training |
| Title | Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results |
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