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Realistic Adversarial Data Augmentation for MR Image Segmentation [article]

Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
<span title="2020-06-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.  ...  MR imaging: bias field.  ...  Discussion and Conclusion In this work, we presented a realistic adversarial data augmentation method to improve the generalization and robustness for neural network-based medical image segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.13322v1">arXiv:2006.13322v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3kbkfjurajgpjeqgl57ki5b24m">fatcat:3kbkfjurajgpjeqgl57ki5b24m</a> </span>
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Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation [article]

Chen Chen, Chen Qin, Cheng Ouyang, Zeju Li, Shuo Wang, Huaqi Qiu, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
<span title="2022-04-23">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation  ...  We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data.  ...  capability for neural network-based medical im- age segmentation of MR images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.03429v2">arXiv:2108.03429v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/m24wykdkbna3fdtq2t5qdlgq2i">fatcat:m24wykdkbna3fdtq2t5qdlgq2i</a> </span>
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On the effectiveness of GAN generated cardiac MRIs for segmentation [article]

Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin, Alain Lalande
<span title="2020-05-22">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
of cine-MR image cardiac segmentation.  ...  We also show that combining data augmentation with our GAN-generated images lead to an improvement in the Dice score of up to 12 percent while allowing for better generalization capabilities on other datasets  ...  In this paper, we propose a combined Variational Autoencoder -Generative Adversarial Network (VAE -GAN) method for producing highly realistic cine-MR images together with their pixel-accurate groundtruth  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.09026v2">arXiv:2005.09026v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a5zp5ae7jjhs3lok2zzl2mwp6u">fatcat:a5zp5ae7jjhs3lok2zzl2mwp6u</a> </span>
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Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning [chapter]

Mauricio Orbes-Arteaga, Thomas Varsavsky, Carole H. Sudre, Zach Eaton-Rosen, Lewis J. Haddow, Lauge Sørensen, Mads Nielsen, Akshay Pai, Sébastien Ourselin, Marc Modat, Parashkev Nachev, M. Jorge Cardoso
<span title="2019-10-13">2019</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains.  ...  Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning.  ...  Future work will focus on removing the need for paired data by finding sufficiently realistic augmentation functions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-33391-1_7">doi:10.1007/978-3-030-33391-1_7</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34109324">pmid:34109324</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7610933/">pmcid:PMC7610933</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nfvxm6muvfcn7c6hsv3trl3dry">fatcat:nfvxm6muvfcn7c6hsv3trl3dry</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321231656/https://kclpure.kcl.ac.uk/portal/files/117778111/1908.05959v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c7/69/c7697f80fc969d89c2667a60da28308964594fcf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-33391-1_7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610933" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

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

Mauricio Orbes-Arteaga and Thomas Varsavsky and Carole H. Sudre and Zach Eaton-Rosen and Lewis J. Haddow and Lauge Sørensen and Mads Nielsen and Akshay Pai and Sébastien Ourselin and Marc Modat and Parashkev Nachev and M. Jorge Cardoso
<span title="2019-09-17">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We provide results on white matter lesion hyperintensity segmentation from brain MRIs using the MICCAI 2017 challenge data as the source domain and two target domains.  ...  Our multi-domain adaptation method utilises a consistency loss combined with adversarial learning.  ...  Future work will focus on removing the need for paired data by finding sufficiently realistic augmentation functions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.05959v2">arXiv:1908.05959v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vzeogii4hjh4tmes6fcsgi6zkm">fatcat:vzeogii4hjh4tmes6fcsgi6zkm</a> </span>
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Learning Data Augmentation for Brain Tumor Segmentation with Coarse-to-Fine Generative Adversarial Networks [article]

Tony C.W Mok, Albert C.S Chung
<span title="2018-08-31">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
While it is often easy for researchers to use data augmentation to expand the size of training sets, constructing and generating generic augmented data that is able to teach the network the desired invariance  ...  In this paper, we propose a novel automatic data augmentation method that uses generative adversarial networks to learn augmentations that enable machine learning based method to learn the available annotated  ...  For brain tumor segmentation, scaling, rotation and flipping have also been applied to multimodal brain MR images for data augmentation [9] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1805.11291v2">arXiv:1805.11291v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ikvianu6jzduxezi5yubsanyym">fatcat:ikvianu6jzduxezi5yubsanyym</a> </span>
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Adversarial Image Synthesis for Unpaired Multi-modal Cardiac Data [chapter]

Agisilaos Chartsias, Thomas Joyce, Rohan Dharmakumar, Sotirios A. Tsaftaris
<span title="">2017</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This paper demonstrates the potential for synthesis of medical images in one modality (e.g. MR) from images in another (e.g. CT) using a CycleGAN [25] architecture.  ...  The synthesis can be learned from unpaired images, and applied directly to expand the quantity of available training data for a given task.  ...  We thank NVIDIA for donating a Titan X GPU.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-68127-6_1">doi:10.1007/978-3-319-68127-6_1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tidos465frdkrglrbziddxbwbu">fatcat:tidos465frdkrglrbziddxbwbu</a> </span>
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Semi-supervised Task-driven Data Augmentation for Medical Image Segmentation [article]

Krishna Chaitanya, Neerav Karani, Christian F. Baumgartner, Ertunc Erdil, Anton Becker, Olivio Donati, Ender Konukoglu
<span title="2020-11-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Unfortunately, the proposed approaches in the literature have not yet yielded significant gains over random data augmentation for image segmentation, where random augmentations themselves do not yield  ...  In this work, we propose a novel task-driven data augmentation method for learning with limited labeled data where the synthetic data generator, is optimized for the segmentation task.  ...  The idea has been applied to various analysis tasks [8] - [14] including MR image segmentation [15] - [17] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.05363v2">arXiv:2007.05363v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ssgnyesdxrdd3eo2kqpd34o4ty">fatcat:ssgnyesdxrdd3eo2kqpd34o4ty</a> </span>
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Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection [article]

Changhee Han, Leonardo Rundo, Ryosuke Araki, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama, Hideaki Hayashi
<span title="2019-03-29">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To fill the data lack in the real image distribution, we synthesize brain contrast-enhanced Magnetic Resonance (MR) images---realistic but completely different from the original ones---using Generative  ...  Due to the lack of available annotated medical images, accurate computer-assisted diagnosis requires intensive Data Augmentation (DA) techniques, such as geometric/intensity transformations of original  ...  Acknowledgment This work was partially supported by the Graduate Program for Social ICT Global Creative Leaders of The University of Tokyo by JSPS.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1903.12564v1">arXiv:1903.12564v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tdeyjwat2fduraxrseb5hzrsd4">fatcat:tdeyjwat2fduraxrseb5hzrsd4</a> </span>
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Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis [article]

Kumar Abhishek, Ghassan Hamarneh
<span title="2019-07-15">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
data augmentation techniques.  ...  Inspired by the immense success of generative adversarial networks (GANs), we propose a GAN-based augmentation of the original dataset in order to improve the segmentation performance.  ...  to augment the dataset for abnormality detection [14] , and generating brain CT images from corresponding brain MR images [23] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1906.05845v2">arXiv:1906.05845v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bxclk4ud4rfttpxhcnzp2iipxm">fatcat:bxclk4ud4rfttpxhcnzp2iipxm</a> </span>
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Combining Shape Priors with Conditional Adversarial Networks for Improved Scapula Segmentation in MR images [article]

Arnaud Boutillon, Bhushan Borotikar, Valérie Burdin, Pierre-Henri Conze
<span title="2020-01-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning.  ...  the model by promoting realistic delineations.  ...  All networks were trained using data augmentation since the amount of available training data was limited.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.08963v3">arXiv:1910.08963v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/g2jxwjypv5a45bk7p7l3qaisa4">fatcat:g2jxwjypv5a45bk7p7l3qaisa4</a> </span>
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GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks [article]

Christopher Bowles, Liang Chen, Ricardo Guerrero, Paul Bentley, Roger Gunn, Alexander Hammers, David Alexander Dickie, Maria Valdés Hernández, Joanna Wardlaw, Daniel Rueckert
<span title="2018-10-25">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Generative Adversarial Networks (GANs) offer a novel way to unlock additional information from a dataset by generating synthetic samples with the appearance of real images.  ...  This paper demonstrates the feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks, leading to improvements in Dice Similarity Coefficient (DSC)  ...  In [20] , the authors use an adversarial network to improve the quality of simulated images, and use these for further training.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.10863v1">arXiv:1810.10863v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/547jbgy4ubh77amr4jaebsazxa">fatcat:547jbgy4ubh77amr4jaebsazxa</a> </span>
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Lesion Mask-based Simultaneous Synthesis of Anatomic and MolecularMR Images using a GAN [article]

Pengfei Guo, Puyang Wang, Jinyuan Zhou, Vishal M. Patel, Shanshan Jiang
<span title="2020-08-26">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
and advanced molecular MR images.  ...  Conventional data augmentation approaches, including flipping, scaling, rotation, and distortion are not capable of generating data with diverse image content.  ...  The synthesized data can be used for data augmentation, particularly for those images with pathological information of malignant gliomas, to improve the performance of segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.14761v3">arXiv:2006.14761v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/os3bosbtdvhmtcpprjsndmmqlu">fatcat:os3bosbtdvhmtcpprjsndmmqlu</a> </span>
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CT-To-MR Conditional Generative Adversarial Networks for Ischemic Stroke Lesion Segmentation [article]

Jonathan Rubin, S. Mazdak Abulnaga
<span title="2019-04-30">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
data inputs to perform ischemic stroke lesion segmentation.  ...  Segmentation networks trained using generated CT-to-MR inputs result in at least some improvement on all metrics used for evaluation, compared with networks that only use CT perfusion input.  ...  Here, GANs were used to augment healthy MR scans with realistic-looking scar tissue.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.13281v1">arXiv:1904.13281v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pzrw6xrmfzfivk42smqa7ocsbq">fatcat:pzrw6xrmfzfivk42smqa7ocsbq</a> </span>
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Adapting to Unseen Vendor Domains for MRI Lesion Segmentation [article]

Brandon Mac, Alan R. Moody, April Khademi
<span title="2021-08-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we investigate the application an unsupervised image translation model to augment MR images from a source dataset to a target dataset.  ...  Recently, image translation models have been proposed to augment data across domains to create synthetic data points.  ...  In and Huo et al. (2019) , the authors have demonstrated the capacity to utilize cyclebased methods to augment between MR and CT images for both data synthesis and segmentation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.06434v1">arXiv:2108.06434v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t2w4ozreybaodhzj52jpa3cxae">fatcat:t2w4ozreybaodhzj52jpa3cxae</a> </span>
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