CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Published in

(2022)

External Links:

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TL;DR

The Cross-Modality Domain Adaptation (crossMoDA) challenge was organised in conjunction with the 24th International Conference on Medical Image Computing and Computer Ass Intervention (MICCAI 2021) and is the first large and multi-class benchmark for unsupervised cross-modality domain Adaptation.

Authors

Nicola Rieke

4 papers

S. Bakas

6 papers

S. Ourselin

17 papers

References85 items

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

C-MADA: unsupervised cross-modality adversarial domain adaptation framework for medical image segmentation

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge

Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation

Published in

(2022)

External Links:

Generate Graph DownloadPDF

TL;DR

Authors

Nicola Rieke

4 papers

S. Bakas

6 papers

S. Ourselin

17 papers

References85 items

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

C-MADA: unsupervised cross-modality adversarial domain adaptation framework for medical image segmentation

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge

Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

Tom Kamiel Magda Vercauteren

11 papers

Sergio Escalera

31 papers

Samuel Joutard

2 papers

Ben Glocker

10 papers

Jorge Cardoso

1 papers

K. Batmanghelich

1 papers

Maria G. Baldeon Calisto

1 papers

Lasse Hansen

2 papers

M. Heinrich

4 papers

Smriti Joshi

1 papers

V. Kashtanova

1 papers

Hyeongyu Kim

1 papers

Satoshi Kondo

1 papers

S. Lai-Yuen

1 papers

Hyungseob Shin

2 papers

B. Shirokikh

2 papers

Guotai Wang

5 papers

Jianghao Wu

1 papers

Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble

Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

S-CUDA: Self-cleansing unsupervised domain adaptation for medical image segmentation

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

Bidirectional cross-modality unsupervised domain adaptation using generative adversarial networks for cardiac image segmentation

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

The Medical Segmentation Decathlon

DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training

Artificial Intelligence Opportunities for Vestibular Schwannoma Management Using Image Segmentation and Clinical Decision Tools.

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Domain Adaptation for Medical Image Analysis: A Survey

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images

Contrastive Learning for Unpaired Image-to-Image Translation

Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation

Scribble-based Domain Adaptation via Co-segmentation

pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization

Source-Relaxed Domain Adaptation for Image Segmentation

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation

An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.

Confident Learning: Estimating Uncertainty in Dataset Labels

Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

Methods and open-source toolkit for analyzing and visualizing challenge results

Correction to: Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

BIAS: Transparent reporting of biomedical image analysis challenges

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Constrained domain adaptation for segmentation

Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

A comparison of semi-automated volumetric vs linear measurement of small vestibular schwannomas

MobileNetV2: Inverted Residuals and Linear Bottlenecks

MRI surveillance of vestibular schwannomas without contrast enhancement: Clinical and economic evaluation

Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Spinal cord grey matter segmentation challenge

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Image-to-Image Translation with Conditional Adversarial Networks

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Revisiting the risks of MRI with Gadolinium based contrast agents—review of literature and guidelines

Domain-Adversarial Training of Neural Networks

Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-similarities

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

SlicerRT: radiation therapy research toolkit for 3D Slicer.

Growth of untreated vestibular schwannoma: a prospective study.

Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Incidence of Vestibular Schwannoma and Neurofibromatosis 2 in the North West of England over a 10-year Period: Higher Incidence than Previously Thought

Automatic segmentation

disease-specific spatial patterns. arXiv preprint arXiv:2001.09313

The journal of machine learning research

The open images dataset v4: Uniﬁed image classiﬁcation, object detection, and visual relationship detection at scale

Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Submitted to Medical Image Analysis (2022) Computing and Computer Assisted Intervention – MICCAI 2019

Why rankings of biomedical image analysis competitions should be interpreted with care

ISLES 2015 ‐ A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Obscuring Surface Anatomy in Volumetric Imaging Data

, Pittsburgh, USA I

Universit´e Cˆote d’Azur, Sophia Antipolis, France V

a School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom b Center for Biomedical Image Computing and Analytics

TL;DR

Authors

References85 items

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

C-MADA: unsupervised cross-modality adversarial domain adaptation framework for medical image segmentation

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge

Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

TL;DR

Authors

References85 items

COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training

C-MADA: unsupervised cross-modality adversarial domain adaptation framework for medical image segmentation

Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An approach for the crossMoDA Challenge

Unsupervised Domain Adaptation in Semantic Segmentation Based on Pixel Alignment and Self-Training

Unsupervised Cross-Modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble

Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation

S-CUDA: Self-cleansing unsupervised domain adaptation for medical image segmentation

Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

Bidirectional cross-modality unsupervised domain adaptation using generative adversarial networks for cardiac image segmentation

Anatomy of Domain Shift Impact on U-Net Layers in MRI Segmentation

Inter Extreme Points Geodesics for End-to-End Weakly Supervised Image Segmentation

The Medical Segmentation Decathlon

DAST: Unsupervised Domain Adaptation in Semantic Segmentation Based on Discriminator Attention and Self-Training

Artificial Intelligence Opportunities for Vestibular Schwannoma Management Using Image Segmentation and Clinical Decision Tools.

Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images

Domain Adaptation for Medical Image Analysis: A Survey

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs

nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation

Unsupervised domain adaptation via CycleGAN for white matter hyperintensity segmentation in multicenter MR images

Contrastive Learning for Unpaired Image-to-Image Translation

Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation

Scribble-based Domain Adaptation via Co-segmentation

pbSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization

Source-Relaxed Domain Adaptation for Image Segmentation

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation

Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns

CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation

An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI.

Confident Learning: Estimating Uncertainty in Dataset Labels

Intramodality Domain Adaptation Using Self Ensembling and Adversarial Training

Methods and open-source toolkit for analyzing and visualizing challenge results

Correction to: Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

BIAS: Transparent reporting of biomedical image analysis challenges

Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning

Constrained domain adaptation for segmentation

Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation

Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss

ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

The conditional

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Unsupervised Cross-Modality Domain Adaptation of ConvNets for Biomedical Image Segmentations with Adversarial Loss

A comparison of semi-automated volumetric vs linear measurement of small vestibular schwannomas

MobileNetV2: Inverted Residuals and Linear Bottlenecks

MRI surveillance of vestibular schwannomas without contrast enhancement: Clinical and economic evaluation

Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Spinal cord grey matter segmentation challenge

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

Image-to-Image Translation with Conditional Adversarial Networks

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Revisiting the risks of MRI with Gadolinium based contrast agents—review of literature and guidelines

Domain-Adversarial Training of Neural Networks

Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols

DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-similarities

The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

MRF-Based Deformable Registration and Ventilation Estimation of Lung CT

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

SlicerRT: radiation therapy research toolkit for 3D Slicer.

Growth of untreated vestibular schwannoma: a prospective study.