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Fetal Brain Tissue Annotation and Segmentation Challenge Results
[article]
2022
arXiv
pre-print
Preserved Fulltext
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 ...
FK-21-125) from University of Zurich, the ZNZ PhD Grant, the EU H2020 Marie Sklodowska-Curie [765148], Austrian Science Fund FWF [P 35189], Vienna Science and Technology Fund WWTF [LS20-065], and the Austrian ...
arXiv:2204.09573v1
fatcat:kdivam6xqbhwpbturdpkyk5e3a
Fetal brain tissue annotation and segmentation challenge results
2023
Preserved Fulltext
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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 ...
Acknowledgments The authors would like to acknowledge funding from the following funding sources: the OPO Foundation, the University Research Priority Project Adaptive Brain Circuits in Development and ...
doi:10.5167/uzh-254102
fatcat:5yhn3jh6xveslh2ni6m7q23qdm
Fetal Tissue Annotation Challenge
[article]
2022
Zenodo
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From a technical standpoint, there are many challenges that an automatic segmentation method of the fetal brain would need to overcome. ...
Automated segmentation and quantification of the highly complex and rapidly changing brain morphology prior to birth in MRI data would improve the diagnostic process, as manual segmentation is both time ...
The results and our corresponding Fetal Tissue Annotation Challenge evaluation of all participating teams will be made publicly available on the challenge website after the conference session. ...
doi:10.5281/zenodo.6362586
fatcat:phlramzd2rb4je7bhtyhrzmbh4
Fetal Tissue Annotation Challenge
[article]
2022
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
From a technical standpoint, there are many challenges that an automatic segmentation method of the fetal brain would need to overcome. ...
Automated segmentation and quantification of the highly complex and rapidly changing brain morphology prior to birth in MRI data would improve the diagnostic process, as manual segmentation is both time ...
The results and our corresponding Fetal Tissue Annotation Challenge evaluation of all participating teams will be made publicly available on the challenge website after the conference session. ...
doi:10.5281/zenodo.6683366
fatcat:3y4tgxdg4jfplozo7iycwgmnp4
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
[article]
2021
arXiv
pre-print
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To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open databases of segmented fetal brains. ...
In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. ...
National Science Foundation (project 205321-182602), and the ZNZ PhD Grant. ...
arXiv:2010.15526v3
fatcat:w4bav4vmzjhcxcoecrfbukghc4
Fetal Brain Tissue Annotation and Segmentation Challenge
[article]
2021
Zenodo
Preserved Fulltext
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The field of fetal MRI has so far been understudied due to challenges in imaging and due to the lack of public, curated, and annotated ground truth data. ...
From a technical standpoint, there are many challenges that an automatic segmentation method of the fetal brain would need to overcome. ...
Fetal Brain Tissue Annotation and Segmentation Challenge
Page 7 of 15
Biomedical Image Analysis ChallengeS (BIAS) Initiative
Test cases do not have the label map. b) State the total number of ...
doi:10.5281/zenodo.4573144
fatcat:z5ni4m36g5g5tcjkybss4akcjq
An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset
2021
Scientific Data
Preserved Fulltext
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The file type is application/pdf.
To facilitate this analysis, automatic multi-tissue fetal brain segmentation algorithms are needed, which in turn requires open datasets of segmented fetal brains. ...
In addition, we quantitatively evaluate the accuracy of several automatic multi-tissue segmentation algorithms of the developing human fetal brain. ...
National Science Foundation (project 205321-182602), and the ZNZ PhD Grant. ...
doi:10.1038/s41597-021-00946-3
pmid:34230489
pmcid:PMC8260784
fatcat:lkxum7s3ibg37dxwvd4ic4evia
A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling
2020
International Conference on Medical Imaging with Deep Learning
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We developed an automated system based on deep neural networks for fast and sensitive 3D image segmentation of cortical gray matter from fetal brain MRI. ...
To address this, we: (i) generated preliminary tissue labels using the Draw-EM algorithm, which uses Expectation-Maximization and was originally designed for tissue segmentation in the neonatal domain; ...
The work was also supported by the NIHR Biomedical Research Centre at Guy's and St Thomas' NHS Trust, London, United Kingdom. ...
dblp:conf/midl/FetitACCDEHHKKM20
fatcat:6jy65ebhzffphnn7ku74ptmggu
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
[article]
2020
arXiv
pre-print
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Our results show that the network can auto-matically segment fetal brain reconstructions into 7 different tissue types, regard-less of reconstruction method used. ...
Segmentation of the developing fetal brain is an important step in quantitative analyses. ...
, Hasler Foundation, the Forschungszentrum für das Kind Grant (FZK) and the PhD Grant from the Neuroscience Center Zurich. ...
arXiv:2009.06275v1
fatcat:5y4rzi3ccncdvneydstkqxqkj4
Domain generalization in fetal brain MRI segmentation with multi-reconstruction augmentation
[article]
2022
arXiv
pre-print
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However, the development of automated segmentation methods is hampered by the scarce availability of fetal brain MRI annotated datasets and the limited variability within these cohorts. ...
In this context, we propose to leverage the power of fetal brain MRI super-resolution (SR) reconstruction methods to generate multiple reconstructions of a single subject with different parameters, thus ...
We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University ...
arXiv:2211.14282v1
fatcat:v5qylsdvnzbevlfbv6c36zqmgu
Learning to segment fetal brain tissue from noisy annotations
[article]
2023
arXiv
pre-print
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Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. ...
Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. ...
DL methods for segmentation of fetal brain into seven tissue types and found that overall DL methods can achieve more accurate results. ...
arXiv:2203.14962v2
fatcat:bii4yakdifaarc3goopvpwebkm
Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
[article]
2024
arXiv
pre-print
Preserved Fulltext
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Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment. ...
However, automated tools face substantial domain shift challenges as they must be robust to highly heterogeneous clinical data, often limited in numbers and lacking annotations. ...
Despite its potential, creating automated pipelines for fetal MRI faces challenges due to the limited availability of annotated datasets and data heterogeneity. ...
arXiv:2403.15103v1
fatcat:xn6zzyoc7vhpld2rebiqycxhay
Automatic segmentation of the intracranialvolume in fetal MR images
[article]
2017
arXiv
pre-print
Preserved Fulltext
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Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). ...
This is challenging because fetal MR images visualize the whole moving fetus and in addition partially visualize the maternal body. ...
Province and the Municipality of Utrecht. ...
arXiv:1708.02282v1
fatcat:ltqt5u7fg5drrfaec567q3yxse
Partial supervision for the FeTA challenge 2021
[article]
2021
arXiv
pre-print
Preserved Fulltext
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The file type is application/pdf.
Perinatal brain MRIs, other than the FeTA challenge data, that are currently publicly available, span normal and pathological fetal atlases as well as neonatal scans. ...
However, perinatal brain MRIs segmented in different datasets typically come with different annotation protocols. This makes it challenging to combine those datasets to train a deep neural network. ...
The original FeTA challenge training data provides 80 fetal brain 3D T2 MRIs with manual manual segmentations of all 7 target tissue types [17] . 40 fetal brain 3D MRIs were reconstructed using MIAL ...
arXiv:2111.02408v1
fatcat:p7wgtsswjrh5vfo4uoe5yekbhe
Segmentation of the cortical plate in fetal brain MRI with a topological loss
[article]
2020
arXiv
pre-print
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An accurate MR image segmentation, and more importantly a topologically correct delineation of the cortical gray matter, is a key baseline to perform further quantitative analysis of brain development. ...
We quantitatively evaluate our method on 18 fetal brain atlases ranging from 21 to 38 weeks of gestation, showing the significant benefits of our method through all gestational ages as compared to a baseline ...
Discarding pathological and non-annotated brains, our training dataset results in 15 healthy fetal brains (see details summarized in Table 1 ). ...
arXiv:2010.12391v2
fatcat:dzyh5z6robblxlleo3e2flks6i
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