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Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
2021
IEEE Transactions on Medical Imaging
Preserved Fulltext
Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. ...
To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease ...
ACKNOWLEDGMENT The authors would like to thank NVIDIA and the Barcelona Super Computing (BSC) centre for providing the necessary computational resources for this work. ...
doi:10.1109/tmi.2021.3090082
pmid:34138702
fatcat:v55qqrms6rcbrewi2foiuvembq
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation
[article]
2020
arXiv
pre-print
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This approach is evaluated on both seen and unseen domains from the M\&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. ...
Deep learning has shown the potential to automate the requisite cardiac structure segmentation. ...
Acknowledgements The authors of this paper declare that the segmentation method they implemented for participation in the M&Ms challenge has not used any pre-trained models nor additional MRI datasets ...
arXiv:2008.11776v1
fatcat:77bk33wkm5ck7lprkx57h6lzqu
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge
2021
Preserved Fulltext
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To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease ...
Cardiac Segmentation (MMs) Challenge, which was recently organized as part of the MICCAI 2020 Conference. ...
ACKNOWLEDGMENT The authors would like to thank NVIDIA and the Barcelona Super Computing (BSC) Centre for providing the necessary computational resources for this work. ...
doi:10.5167/uzh-214495
fatcat:po3ijktc5jbmhacvy2jatwaguu
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge
[article]
2020
Zenodo
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The M&MS challenge is the first international competition to date on cardiac image segmentation combining data from different centres, vendors, diseases and countries at the same time. ...
This is the challenge design document for the "Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge", accepted for MICCAI 2020. ...
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge Page 3 of 12 b) Report the platform (e.g. grand-challenge.org) used to run the challenge. ...
doi:10.5281/zenodo.3886268
fatcat:kj6vzgvqtjcu3l7dhcdjuiij4m
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge
[article]
2020
Zenodo
Preserved Fulltext
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The file type is application/pdf.
The M&MS challenge is the first international competition to date on cardiac image segmentation combining data from different centres, vendors, diseases and countries at the same time. ...
This is the challenge design document for the "Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge", accepted for MICCAI 2020. ...
Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge Page 3 of 12 b) Report the platform (e.g. grand-challenge.org) used to run the challenge. ...
doi:10.5281/zenodo.3715889
fatcat:nml2woh6ibbzzfcdcda2wfstnq
Abstract: Studying Robustness of Semantic Segmentation under Domain Shift in Cardiac MRI
[chapter]
2021
Informatik aktuell
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In this work, we systematically study challenges and opportunities of domain transfer across images from multiple clinical centres and scanner vendors. ...
Our proposed method ranked first at the Multi-Centre, ...
Our proposed method ranked first at the Multi-Centre,
Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms). ...
doi:10.1007/978-3-658-33198-6_64
fatcat:d63kby5hivf5blmbdzfeac2s3q
Multi-view SA-LA Net: A framework for simultaneous segmentation of RV on multi-view cardiac MR Images
[article]
2021
arXiv
pre-print
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Multi-view SA-LA model was extensively evaluated on the MICCAI 2021 Multi- Disease, Multi-View, and Multi- Centre RV Segmentation Challenge dataset (M&Ms-2021). ...
M&Ms-2021 dataset consists of multi-phase, multi-view cardiac MR images of 360 subjects acquired at four clinical centers with three different vendors. ...
The most recent challenge hosted from MICCAI platform was the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Image Segmentation Challenge (M&Ms-2020). ...
arXiv:2110.00682v1
fatcat:2oy7ytmuhjfvjazr4kn3mrpfz4
Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2)
[article]
2021
Zenodo
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The M&Ds challenge is the first international competition to date on cardiac right ventricle segmentation and diagnosis combining data from different centres, vendors and diseases at the same time. ...
It will evaluate the generalisation ability of machine/deep learning and cross-domain transfer learning techniques for abnormal cardiac imaging right ventricle segmentation and righ ventricle related disease ...
Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI (M&Ms-2) Provide a timetable for the challenge. ...
doi:10.5281/zenodo.4573983
fatcat:wiilsibbzzbulefyxtbufmpzvi
Disentangled Representations for Domain-generalized Cardiac Segmentation
[article]
2020
arXiv
pre-print
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Robust cardiac image segmentation is still an open challenge due to the inability of the existing methods to achieve satisfactory performance on unseen data of different domains. ...
In this paper, we propose two data augmentation methods that focus on improving the domain adaptation and generalization abilities of state-to-the-art cardiac segmentation models. ...
Experiments on the diverse dataset from the STACOM 2020 Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms challenge) show the superiority of the proposed augmentation ...
arXiv:2008.11514v1
fatcat:fysrlawdqrdexay3qrii6abs5q
Multi-center, multi-vendor automated segmentation of left ventricular anatomy in contrast-enhanced MRI
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2110.07360v1 fatcat:vczajyvo5ngp7mfrr4vvcxwgmm
This work investigates for the first time multi-center and multi-vendor LV segmentation in LGE-MRI, by proposing, implementing and evaluating in detail several strategies to enhance model generalizability ...
The results obtained based on a new multi-center LGE-MRI dataset acquired in four clinical centers in Spain, France and China, show that the combination of data augmentation and transfer learning can lead ...
Entitled "Multi-Center, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms)", the study demonstrated that single-center, single-vendor neural networks do not generalize naturally when segmenting ...
arXiv:2110.07360v2
fatcat:6udrjjyurra6va6lnywtlrtpay
A Data-scalable Transformer for Medical Image Segmentation: Architecture, Model Efficiency, and Benchmark
[article]
2022
arXiv
pre-print
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The key designs incorporate desirable inductive bias, hierarchical modeling with linear-complexity attention, and multi-scale feature fusion in a spatially and semantically global manner. ...
To tackle these challenges, we present UTNetV2 as a data-scalable Transformer towards generalizable medical image segmentation. ...
The M&Ms (Multi-centre, multi-vendor, and multi-disease cardiac segmentation) and M&Ms-2 (Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI) 40 are challenges in ...
arXiv:2203.00131v3
fatcat:dmuh4yga4rahzjjdy4ttg7eei4
Style Curriculum Learning for Robust Medical Image Segmentation
[article]
2021
arXiv
pre-print
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Extensive experiments on the public M&Ms Challenge dataset demonstrate that our proposed framework can generalise deep models well to unknown distributions and achieve significant improvements in segmentation ...
This is particularly pronounced in multi-centre studies involving data acquired using multi-vendor scanners, with variations in acquisition protocols. ...
Extensive experiments on public data from the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge [3] show that our method outperforms all 2D methods, and is on par with ...
arXiv:2108.00402v1
fatcat:t3ogxbgauvhhvkc7ifoqhjadcm
Automatic Segmentation of Left Ventricle in Cardiac Magnetic Resonance Images
[article]
2022
arXiv
pre-print
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We report validations on 320 scans containing 5,273 annotated slices which are publicly available through the Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac Segmentation (M&Ms) Challenge. ...
Segmentation of the left ventricle in cardiac magnetic resonance imaging MRI scans enables cardiologists to calculate the volume of the left ventricle and subsequently its ejection fraction. ...
We would also like to thank the organizers of the M&Ms challenge for providing us with a diverse collection of cardiac MR studies. ...
arXiv:2201.12805v1
fatcat:7qxylovxojaa5agajzrvonu3ay
Style-invariant Cardiac Image Segmentation with Test-time Augmentation
[article]
2020
arXiv
pre-print
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Second, we build up a robust cardiac segmentation model based on the U-Net structure. ...
In this paper, we propose a novel style-invariant method for cardiac image segmentation. ...
The framework was trained and evaluated on the Multi-Centre, Multi-Vendor & Multi-Disease Cardiac Image Segmentation Challenge (M&Ms 2020) dataset [2] . ...
arXiv:2009.12193v1
fatcat:xejpzshrgzeapaaxgte2j7d76m
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
2021
Algorithms
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To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI ...
Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. ...
Acknowledgments: We would like to acknowledge Ivan Porcherot for the tremendous work in annotating the data sets.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/a14070212
fatcat:b3hsk6sj25adhl7qkh7xsxceca
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