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A Learning Strategy for Contrast-agnostic MRI Segmentation [article]

Benjamin Billot, Douglas Greve, Koen Van Leemput, Bruce Fischl, Juan Eugenio Iglesias, Adrian V. Dalca
<span title="2021-04-08">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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https://web.archive.org/web/20200826155647/https://arxiv.org/pdf/2003.01995v2.pdf

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<span title="2020-04-27">2020</span> <span class="release-stage" >pre-print</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.01995v2">arXiv:2003.01995v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/444hd7td7rad5okcktph532fwy">fatcat:444hd7td7rad5okcktph532fwy</a> </span>

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<span title="2020-03-04">2020</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.01995v1">arXiv:2003.01995v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k4bun5sxpbhklnhklbdfngy7d4">fatcat:k4bun5sxpbhklnhklbdfngy7d4</a> </span>
We present a deep learning strategy that enables, for the first time, contrast-agnostic semantic segmentation of completely unpreprocessed brain MRI scans, without requiring additional training or fine-tuning  ...  Because each mini-batch has a different synthetic contrast, the final network is not biased towards any MRI contrast.  ...  Acknowledgments This research was supported by the European Research Council (ERC Starting Grant 677697, project BUNGEE-TOOLS), and by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.01995v3">arXiv:2003.01995v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/h6siaq4dnbdktfqk7b3riwvs3u">fatcat:h6siaq4dnbdktfqk7b3riwvs3u</a> </span>
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Citation

Benjamin Billot, Douglas Greve, Koen Van Leemput, Bruce Fischl, Juan Eugenio Iglesias, Adrian V. Dalca. "A Learning Strategy for Contrast-agnostic MRI Segmentation." arXiv (2021)

Learning image registration without images [article]

Malte Hoffmann, Benjamin Billot, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca
<span title="2020-06-26">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2020-04-21">2020</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.10282v1">arXiv:2004.10282v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sdh7ixthovdcrkhu4zmoxk3254">fatcat:sdh7ixthovdcrkhu4zmoxk3254</a> </span>
<span title="2021-10-07">2021</span> <span class="release-stage">accepted</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.10282v3">arXiv:2004.10282v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/23gfhnala5b4zel7hyr4oe6xra">fatcat:23gfhnala5b4zel7hyr4oe6xra</a> </span>
<span title="2022-03-03">2022</span> <span class="release-stage">accepted</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.10282v4">arXiv:2004.10282v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xvwnmonrkfgwnexzfqbkacbo3i">fatcat:xvwnmonrkfgwnexzfqbkacbo3i</a> </span>
We introduce a learning strategy for contrast-invariant image registration without requiring imaging data.  ...  We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary image contrasts without the need to retrain for new modalities.  ...  BF has a financial interest in CorticoMetrics, a company focusing on brain imaging and measurement technologies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.10282v2">arXiv:2004.10282v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/odbnje7e6zhzhjwkok4kaujjwa">fatcat:odbnje7e6zhzhjwkok4kaujjwa</a> </span>
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Citation

Malte Hoffmann, Benjamin Billot, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca. "Learning image registration without images." arXiv (2020)

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation [article]

Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng, Yang Liu, Jiayu Zhou, Yao Zhao
<span title="2021-03-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2022-01-16">2022</span> <span class="release-stage">accepted</span> <span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2103.08454v2">arXiv:2103.08454v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jk7p77u6sre2dj6vzimltbshdq">fatcat:jk7p77u6sre2dj6vzimltbshdq</a> </span>
To address this issue, we propose in this paper a novel margin preserving self-paced contrastive Learning (MPSCL) model for cross-modal medical image segmentation.  ...  To enhance the supervision for contrastive learning, more informative pseudo-labels are generated in target domain in a self-paced way, thus benefiting the category-aware distribution alignment for UDA  ...  Then, following the self-spaced learning scheme which has been found effective for gradually learning a robust model [20] - [22] , a suitable selection strategy is explored to remove error-prone predictions  ... 
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Citation

Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng, Yang Liu, Jiayu Zhou, Yao Zhao. "Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation." arXiv (2021)

AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs [article]

Lei Li and Veronika A. Zimmer and Julia A. Schnabel and Xiahai Zhuang
<span title="2021-07-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-06-16">2021</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08727v1">arXiv:2106.08727v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ndzugvwmwjck5ag3adtorgvhiy">fatcat:ndzugvwmwjck5ag3adtorgvhiy</a> </span>
<span title="2021-06-18">2021</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08727v2">arXiv:2106.08727v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xaonnbflengqhnmbkmyrbgja3y">fatcat:xaonnbflengqhnmbkmyrbgja3y</a> </span>
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation.  ...  To evaluate the domain generalization ability of models on the LA segmentation task, we employ four commonly used semantic segmentation networks for the LA segmentation from multi-center LGE MRIs.  ...  JA Schnabel and VA Zimmer would like to acknowledge funding from a Wellcome Trust IEH Award (WT 102431), an EPSRC programme grant (EP/P001009/1), and the Wellcome/EPSRC Center for Medical Engineering (  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.08727v3">arXiv:2106.08727v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eixdzqohwna33oqhytqpydjp4e">fatcat:eixdzqohwna33oqhytqpydjp4e</a> </span>
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Citation

Lei Li and Veronika A. Zimmer and Julia A. Schnabel and Xiahai Zhuang. "AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs." arXiv (2021)

SynthSeg: Domain Randomisation for Segmentation of Brain Scans of any Contrast and Resolution [article]

Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias
<span title="2021-12-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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<span title="2021-07-20">2021</span> <span class="release-stage" >pre-print</span>
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Here we introduce SynthSeg, the first segmentation CNN agnostic to contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations.  ...  Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data.  ...  Dalca, “A Learning Strategy for Contrast-agnostic MRI with application to confocal microscopy images of bee brains,” Segmentation,” in Medical Imaging with Deep Learning, 2020, pp.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.09559v2">arXiv:2107.09559v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lxppdyslkffg7hxggk3ztcs73m">fatcat:lxppdyslkffg7hxggk3ztcs73m</a> </span>
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Citation

Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen Van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias. "SynthSeg: Domain Randomisation for Segmentation of Brain Scans of any Contrast and Resolution." arXiv (2021)

Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation [article]

Hao Zheng, Jun Han, Hongxiao Wang, Lin Yang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen
<span title="2021-07-10">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We develop a new loss function by combining contrastive loss and classification loss and pretrain an encoder-decoder architecture for segmentation tasks.  ...  A large labeled dataset is a key to the success of supervised deep learning, but for medical image segmentation, it is highly challenging to obtain sufficient annotated images for model training.  ...  imaging techniques or expensive annotations (e.g., 3D(+T) images), which suppresses self-supervised pre-training and hinders representation learning using a single dataset. (2) The contrastive strategy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.04886v1">arXiv:2107.04886v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rzi2htp4izgezaxb3i43hngk2m">fatcat:rzi2htp4izgezaxb3i43hngk2m</a> </span>
<span class="ui label basic orange small" title="Open Access">Open Access</span>
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Citation

Hao Zheng, Jun Han, Hongxiao Wang, Lin Yang, Zhuo Zhao, Chaoli Wang, Danny Z. Chen. "Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation." arXiv (2021)

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation [article]

Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai
<span title="2020-06-23">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.  ...  Here we propose a novel deep generative model-based framework for quality control of cardiac MRI segmentation.  ...  In contrast, the proposed method maintained a high prediction accuracy against domain shift. This indicates the advantage of a generative model-based framework for generalisation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.13379v1">arXiv:2006.13379v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jiuuq3wwybee5gmoddmn4kmyg4">fatcat:jiuuq3wwybee5gmoddmn4kmyg4</a> </span>
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Citation

Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai. "Deep Generative Model-based Quality Control for Cardiac MRI Segmentation." arXiv (2020)

Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training

Siddhesh Thakur, Jimit Doshi, Sarthak Pati, Saima Rathore, Chiharu Sako, Michel Bilello, Sung Min Ha, Gaurav Shukla, Adam Flanders, Aikaterini Kotrotsou, Mikhail Milchenko, Spencer Liem (+12 others)
<span title="2020-06-27">2020</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sa477uo7lveh7hchpikpixop5u" style="color: black;">NeuroImage</a> </i> &nbsp;

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In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular  ...  Our results indicate that the modality-agnostic approach obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.  ...  a widely applicable generic tool for segmentation of brain images with tumors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neuroimage.2020.117081">doi:10.1016/j.neuroimage.2020.117081</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32603860">pmid:32603860</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7597856/">pmcid:PMC7597856</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xu4tqsxkivbitgjyvsmv4iqvru">fatcat:xu4tqsxkivbitgjyvsmv4iqvru</a> </span>
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Siddhesh Thakur, Jimit Doshi, Sarthak Pati, Saima Rathore, Chiharu Sako, Michel Bilello, Sung Min Ha, Gaurav Shukla, Adam Flanders, Aikaterini Kotrotsou, Mikhail Milchenko, Spencer Liem, Gregory S. Alexander, Joseph Lombardo, Joshua D. Palmer, Pamela LaMontagne, Arash Nazeri, Sanjay Talbar, Uday Kulkarni, Daniel Marcus, Rivka Colen, Christos Davatzikos, Guray Erus, Spyridon Bakas. "Brain Extraction on MRI Scans in Presence of Diffuse Glioma: Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training." NeuroImage (2020) 117081

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review

Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh, Riccardo De Feo, Mithilesh Prakash, Robert Ciszek, Jussi Tohka
<span title="2021-04-01">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/au3ye363lzbdroopx7rzfyv63m" style="color: black;">Journal of Imaging</a> </i> &nbsp;

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In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners.  ...  We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI.  ...  Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains.  ... 
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Juan Miguel Valverde, Vandad Imani, Ali Abdollahzadeh, Riccardo De Feo, Mithilesh Prakash, Robert Ciszek, Jussi Tohka. "Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review." Journal of Imaging 7.4 (2021) 66

NeXtQSM – A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data [article]

Francesco Cognolato, Kieran O'Brien, Jin Jin, Simon Robinson, Frederik B. Laun, Markus Barth, Steffen Bollmann
<span title="2021-07-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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We demonstrate that NeXtQSM overcomes the limitations of previous model-agnostic deep learning methods and show that NeXtQSM offers a complete deep learning based pipeline for computing robust, fast and  ...  We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational  ...  Acknowledgments The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy (NCRIS) capability  ... 
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Francesco Cognolato, Kieran O'Brien, Jin Jin, Simon Robinson, Frederik B. Laun, Markus Barth, Steffen Bollmann. "NeXtQSM – A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data." arXiv (2021)

Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

Anke Meyer-Bäse, Lia Morra, Uwe Meyer-Bäse, Katja Pinker
<span title="2020-08-28">2020</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r5ecdsgx3bafnpd5wn625laa2i" style="color: black;">Contrast Media &amp; Molecular Imaging</a> </i> &nbsp;

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Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.  ...  The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI.  ...  Acknowledgments e authors would like to thank Angelo Laudani for assistance in the retrieval of scientific literature.  ... 
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Citation

Anke Meyer-Bäse, Lia Morra, Uwe Meyer-Bäse, Katja Pinker. "Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging." Contrast Media & Molecular Imaging (2020) 1-18

Radiomics and radiogenomics for precision radiotherapy

Jia Wu, Khin Khin Tha, Lei Xing, Ruijiang Li
<span title="2018-01-27">2018</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/webgvpku5bbjbbgtghz7ntp3we" style="color: black;">Journal of Radiation Research</a> </i> &nbsp;

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We will also present some examples of the current results and some emerging paradigms in radiomics and radiogenomics for clinical oncology, with a focus on potential applications in radiotherapy.  ...  A number of studies have demonstrated that a deeper radiomic analysis can reveal novel image features that could provide useful diagnostic, prognostic or predictive information, improving upon currently  ...  In the near future, deep-learning-based auto-segmentation tools that are robust enough for routine radiomics applications should be available.  ... 
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Citation

Jia Wu, Khin Khin Tha, Lei Xing, Ruijiang Li. "Radiomics and radiogenomics for precision radiotherapy." Journal of Radiation Research 59.suppl_1 (2018) i25-i31

Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond

Guang Yang, Qinghao Ye, Jun Xia
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/u3qqmkiofjejrnpdxh3hdgssm4" style="color: black;">Information Fusion</a> </i> &nbsp;

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The XAI becomes more and more crucial for deep learning powered applications, especially for medical and healthcare studies, although in general these deep neural networks can return an arresting dividend  ...  Many of the machine learning algorithms cannot manifest how and why a decision has been cast. This is particularly true of the most popular deep neural network approaches currently in use.  ...  MRI modality (e.g., T1, T1 post-contrast and FLAIR), respectively.  ... 
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Guang Yang, Qinghao Ye, Jun Xia. "Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond." Information Fusion (2021) 29-52

Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges

Qian Chen, Tianyi Xia, Mingyue Zhang, Nengzhi Xia, Jinjin Liu, Yunjun Yang
<span title="2021-02-01">2021</span> <i title="Aging and Disease"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2gqkige7abgxjkg5w2pt2yo6em" style="color: black;">Aging and Disease</a> </i> &nbsp;

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Stroke is a leading cause of disability and mortality worldwide, resulting in substantial economic costs for post-stroke care each year.  ...  Currently, radiomic analysis has shown promise for a variety of applications in stroke, including the diagnosis of stroke lesions, early prediction of outcomes, and evaluation for long-term prognosis.  ...  In contrast to supervised learning, unsupervised learning approaches (e.g., k-means clustering) can construct a model without clinical labels and using a limited sample size.  ... 
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Qian Chen, Tianyi Xia, Mingyue Zhang, Nengzhi Xia, Jinjin Liu, Yunjun Yang. "Radiomics in Stroke Neuroimaging: Techniques, Applications, and Challenges." Aging and Disease 12.1 (2021) 143

Radiomics & Radiology: A Critical Step towards Integrated Healthcare

Mayur Pankhania, Aditya Mehta
<span title="2020-12-30">2020</span> <i title="Society for Healthcare &amp; Research Development"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qlq2aafcdrgppexxsc6ay4wzva" style="color: black;">Asian Journal of Medical Radiological Research</a> </i> &nbsp;

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for therapeutic judgements.  ...  It has great potential in creating a paradigm shift in the way healthcare is delivered and perceived.  ...  Segmentation and Interpretation of Tumour Segmentation is a crucial step where the acquired image is segmented to obtain Regions of interest (ROI).  ... 
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Mayur Pankhania, Aditya Mehta. "Radiomics & Radiology: A Critical Step towards Integrated Healthcare." Asian Journal of Medical Radiological Research (2020) 23-30

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