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Robust White Matter Hyperintensity Segmentation on Unseen Domain [article]

Xingchen Zhao, Anthony Sicilia, Davneet Minhas, Erin O'Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, Seong Jae Hwang
2021 arXiv   pre-print

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2021 pre-print arXiv:2102.06650v1 fatcat:qhligkknc5hvtnoenku26o3cva
We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset.  ...  Then, we show drastic improvements of WMH prediction on an unseen target domain.  ...  DeepAll MixDANN CONCLUSION We investigate the domain generalizability of a WMH segmentation deep model to be trained on sources and operate well on an unseen target.  ... 
arXiv:2102.06650v2 fatcat:wagyvkjetvhsxie45x325ody6i
Open Access Multiple Versions
Citation

Xingchen Zhao, Anthony Sicilia, Davneet Minhas, Erin O'Connor, Howard Aizenstein, William Klunk, Dana Tudorascu, Seong Jae Hwang. "Robust White Matter Hyperintensity Segmentation on Unseen Domain." arXiv (2021)

White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections [article]

Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura
2018 arXiv   pre-print

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Segmentation and quantification of white matter hyperintensities (WMHs) are of great importance in studying and understanding various neurological and geriatric disorders.  ...  Although automatic methods have been proposed for WMH segmentation on magnetic resonance imaging (MRI), manual corrections are often necessary to achieve clinically practical results.  ...  INTRODUCTION White matter hyperintensities (WMHs) are brain areas in the cerebral white matter with increased signal intensity on T2-weighted or fluid-attenuated inversion recovery (FLAIR) magnetic resonance  ... 
arXiv:1803.06782v1 fatcat:k55h6vxevjeqjmfing7qjmpciy
Citation

Dakai Jin, Ziyue Xu, Adam P. Harrison, Daniel J. Mollura. "White matter hyperintensity segmentation from T1 and FLAIR images using fully convolutional neural networks enhanced with residual connections." arXiv (2018)

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
2019 Lecture Notes in Computer Science  

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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.  ...  Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain.  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of one Titan Xp.  ... 
doi:10.1007/978-3-030-33391-1_7 pmid:34109324 pmcid:PMC7610933 fatcat:nfvxm6muvfcn7c6hsv3trl3dry
Citation

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. "Multi-domain Adaptation in Brain MRI Through Paired Consistency and Adversarial Learning." Lecture Notes in Computer Science (2019) 54-62

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
2019 arXiv   pre-print

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2019 pre-print
arXiv:1908.05959v1 fatcat:anp2f43rtzezjie4pq7vojjtc4
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.  ...  Recent work in domain adaptation addresses this challenge and successfully leverages labeled data in a source domain to perform well on an unlabeled target domain.  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of one Titan Xp.  ... 
arXiv:1908.05959v2 fatcat:vzeogii4hjh4tmes6fcsgi6zkm
Multiple Versions
Citation

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. "Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning." arXiv (2019)

Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies

Vamsi Ithapu, Vikas Singh, Christopher Lindner, Benjamin P. Austin, Chris Hinrichs, Cynthia M. Carlsson, Barbara B. Bendlin, Sterling C. Johnson
2014 Human Brain Mapping  

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Key words: white matter hyperintensities; support vector machines; random forests; segmentation r r r Ithapu et al. r r 4220 r  ...  Through extensive evaluations on healthy middle-aged and older adults who vary in AD risk, we show that our methods are reliable and robust in segmenting hyperintense regions.  ...  We formulate the task of White Matter Hyperintensities (WMH) segmentation as a supervised inference problem.  ... 
doi:10.1002/hbm.22472 pmid:24510744 pmcid:PMC4107160 fatcat:lknptbzgabc3nbueh4vrb6xnxa
DOAJ
Citation

Vamsi Ithapu, Vikas Singh, Christopher Lindner, Benjamin P. Austin, Chris Hinrichs, Cynthia M. Carlsson, Barbara B. Bendlin, Sterling C. Johnson. "Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies." Human Brain Mapping 35.8 (2014) n/a-n/a

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation [article]

Parisa Mojiri Forooshani, Mahdi Biparva, Emmanuel E. Ntiri, Joel Ramirez, Lyndon Boone, Melissa F. Holmes, Sabrina Adamo, Fuqiang Gao, Miracle Ozzoude, Christopher J.M. Scott, Dar Dowlatshahi, Jane M. Lawrence-Dewar (+15 others)
2021 bioRxiv   pre-print

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White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia.  ...  We further validated our models highlighting their robustness on ′clinical adversarial cases′ simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences  ...  Introduction Clinical Motivation White matter hyperintensities (WMH) are commonly observed MRI-based biomarkers of cerebral small vessel disease and have been associated with aging and neurodegenerative  ... 
doi:10.1101/2021.08.18.456666 fatcat:bnubw7he4nf2pk77ljn7blj27m
Citation

Parisa Mojiri Forooshani, Mahdi Biparva, Emmanuel E. Ntiri, Joel Ramirez, Lyndon Boone, Melissa F. Holmes, Sabrina Adamo, Fuqiang Gao, Miracle Ozzoude, Christopher J.M. Scott, Dar Dowlatshahi, Jane M. Lawrence-Dewar, Donna Kwan, Anthony E. Lang, Karine Marcotte, carol Leonard, Elizabeth Rochon, Chris Heyn, Robert Bartha, Stephen Strother, Jean-Claude Tardif, Sean Symons, Mario Masellis, Richard H. Swartz, Alan Moody, Sandra E. Black, Maged Goubran. "Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation." bioRxiv (2021)

Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Kaisar Kushibar, Mostafa Salem, Sergi Valverde, Àlex Rovira, Joaquim Salvi, Arnau Oliver, Xavier Lladó
2021 Frontiers in Neuroscience  

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brain structure segmentation; and (2) white matter hyperintensities segmentation.  ...  Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster  ...  White Matter Hyperintensity Lesion Segmentation Motivation White Matter Hyperintensities are brain lesions that appear bright in T2-weighted and Fluid Attenuated Inversion Recovery (FLAIR) sequences.  ... 
doi:10.3389/fnins.2021.608808 pmid:33994917 pmcid:PMC8116893 fatcat:le26kk4qbfh3dhwnofsmxybdpa
DOAJ
Citation

Kaisar Kushibar, Mostafa Salem, Sergi Valverde, Àlex Rovira, Joaquim Salvi, Arnau Oliver, Xavier Lladó. "Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation." Frontiers in Neuroscience (2021) 608808

Hybrid Attention Densely Connected Ensemble Framework for Lesion Segmentation from Magnetic Resonance Images

Beibei Hou, Beibei Hou, Xin Xu, Guixia Kang, Guixia Kang, Yuan Tang, Chuan Hu
2020 IEEE Access  

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As shown in Fig.1 , WMHs are visible as hyperintense regions on the fluid attenuated inversion recovery (FLAIR) modality and hypointense regions on the T1-weighted (T1) modality within the white matter  ...  INTRODUCTION W HITE matter hyperintensities (WMHs) are one of the main consequences of small blood vessel disease, which plays a vital role in the assessment of dementia, stroke , and aging [1] .  ... 
doi:10.1109/access.2020.3030913 fatcat:6ga5sluqjncepibwz5yaaagpyi
DOAJ
Citation

Beibei Hou, Beibei Hou, Xin Xu, Guixia Kang, Guixia Kang, Yuan Tang, Chuan Hu. "Hybrid Attention Densely Connected Ensemble Framework for Lesion Segmentation from Magnetic Resonance Images." IEEE Access (2020) 1-1

Knowledge distillation for semi-supervised domain adaptation [article]

Mauricio Orbes-Arteaga and Jorge Cardoso and Lauge Sørensen and Christian Igel and Sebastien Ourselin and Marc Modat and Mads Nielsen and Akshay Pai
2019 arXiv   pre-print

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The proposed method is compared to ADA for segmentation of white matter hyperintensities (WMH) in magnetic resonance imaging (MRI) scans generated by scanners that are not a part of the training set.  ...  As a result, their performance is significantly lower on data from unseen sources compared to the performance on data from the same source as the training data.  ...  Through our evaluation, we show that the proposed KD is generally able to achieve better dice scores in segmenting white matter hyperintensities (WMH) on datasets that are not a part of the training data  ... 
arXiv:1908.07355v1 fatcat:3c2wd6j3ybekhcurmx7z2gadgm
Citation

Mauricio Orbes-Arteaga and Jorge Cardoso and Lauge Sørensen and Christian Igel and Sebastien Ourselin and Marc Modat and Mads Nielsen and Akshay Pai. "Knowledge distillation for semi-supervised domain adaptation." arXiv (2019)

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications [article]

Yang Ma, Chaoyi Zhang, Mariano Cabezas, Yang Song, Zihao Tang, Dongnan Liu, Weidong Cai, Michael Barnett, Chenyu Wang
2022 arXiv   pre-print

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2021 pre-print
arXiv:2104.10029v1 fatcat:52hc5z4hereabne4ibbfzfh24a
2021 pre-print
arXiv:2104.10029v2 fatcat:kytxvldjmjdtzeplrgr3hxh7de
Multiple sclerosis (MS) is a chronic inflammatory and degenerative disease of the central nervous system, characterized by the appearance of focal lesions in the white and gray matter that topographically  ...  Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.  ...  Compared with normal appearing white matter, MS lesions appear hypointense on T1-w images; and are usually hyperintense on T2-w, PD-w, and FLAIR images (Fig. 1 ).  ... 
arXiv:2104.10029v3 fatcat:elds3foafrdc5ireld5wahp5ra
Open Access Multiple Versions
Citation

Yang Ma, Chaoyi Zhang, Mariano Cabezas, Yang Song, Zihao Tang, Dongnan Liu, Weidong Cai, Michael Barnett, Chenyu Wang. "Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications." arXiv (2022)

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

Hongwei Li, Timo Loehr, Anjany Sekuboyina, Jianguo Zhang, Benedikt Wiestler, Bjoern Menze
2020 arXiv   pre-print

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2020 pre-print
arXiv:2001.09313v2 fatcat:sj72j6tgmrcjnebrav6nw64zy4
2020 pre-print
arXiv:2001.09313v1 fatcat:34ee3mgtdrauxpwvgwzdzyy2da
We demonstrate that recalibrating the deep networks on a few unlabeled images from the target domain improves the segmentation accuracy significantly.  ...  , but by re-calibrating the networks on few images from the target domain.  ...  Task 1 : 1 White matter hyperintensities (WMH) segmentation. We use the public datasets of MICCAI White Matter Hyperintensities Segmentation Challenge 2017 [36] including three centres.  ... 
arXiv:2001.09313v3 fatcat:hyjylq4z6rdcxlkzoz4cmthn6i
Multiple Versions
Citation

Hongwei Li, Timo Loehr, Anjany Sekuboyina, Jianguo Zhang, Benedikt Wiestler, Bjoern Menze. "Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns." arXiv (2020)

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification [article]

Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie (+91 others)
2021 arXiv   pre-print

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2021 pre-print
arXiv:2107.02314v1 fatcat:i6md2ovygfajresf46c2zeiyde
Since its inception, BraTS has been focusing on being a common benchmarking venue for brain glioma segmentation algorithms, with well-curated multi-institutional multi-parametric magnetic resonance imaging  ...  Specifically, the two tasks that BraTS 2021 focuses on are: a) the segmentation of the histologically distinct brain tumor sub-regions, and b) the classification of the tumor's O[6]-methylguanine-DNA methyltransferase  ...  Acknowledgments Success of any challenge in the medical domain depends upon the quality of well annotated multi-institutional datasets.  ... 
arXiv:2107.02314v2 fatcat:6jes7hrrxzempat6gilndwvpgm
Open Access Multiple Versions
Citation

Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, James Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, Ahmed W. Moawad, Luiz Otavio Coelho, Olivia McDonnell, Elka Miller, Fanny E. Moron, Mark C. Oswood, Robert Y. Shih, Loizos Siakallis, Yulia Bronstein, James R. Mason, Anthony F. Miller, Gagandeep Choudhary, Aanchal Agarwal, Cristina H. Besada, Jamal J. Derakhshan, Mariana C. Diogo, Daniel D. Do-Dai, Luciano Farage, John L. Go, Mohiuddin Hadi, Virginia B. Hill, Michael Iv, David Joyner, Christie Lincoln, Eyal Lotan, Asako Miyakoshi, Mariana Sanchez-Montano, Jaya Nath, Xuan V. Nguyen, Manal Nicolas-Jilwan, Johanna Ortiz Jimenez, Kerem Ozturk, Bojan D. Petrovic, Chintan Shah, Lubdha M. Shah, Manas Sharma, Onur Simsek, Achint K. Singh, Salil Soman, Volodymyr Statsevych, Brent D. Weinberg, Robert J. Young, Ichiro Ikuta, Amit K. Agarwal, Sword C. Cambron, Richard Silbergleit, Alexandru Dusoi, Alida A. Postma, Laurent Letourneau-Guillon, Gloria J. Guzman Perez-Carrillo, Atin Saha, Neetu Soni, Greg Zaharchuk, Vahe M. Zohrabian, Yingming Chen, Milos M. Cekic, Akm Rahman, Juan E. Small, Varun Sethi, Christos Davatzikos, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva-Meyer, John B. Freymann, Justin S. Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Bjoern Menze, Adam E. Flanders, Spyridon Bakas. "The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification." arXiv (2021)

Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience [article]

Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Lasya P. Sreepada, Sai Spandana Chintapalli, Dushyant Sahoo, Ioanna Skampardoni, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen (+1 others)
2022 arXiv   pre-print

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Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields.  ...  This review appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis  ...  Acknowledgment This work was supported by the National Institute on Aging (grant numbers RF1AG054409 and U01AG068057), the National Institute of Mental Health (grant number R01MH112070), the National Cancer  ... 
arXiv:2206.07081v1 fatcat:463hupobs5fp7okuguhp6tmzia
Open Access
Citation

Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Lasya P. Sreepada, Sai Spandana Chintapalli, Dushyant Sahoo, Ioanna Skampardoni, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, Christos Davatzikos. "Applications of Generative Adversarial Networks in Neuroimaging and Clinical Neuroscience." arXiv (2022)

Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine [article]

Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst, Serge O. Dumoulin, Jan-Jakob Sonke, Frans M. Vos, Matthan W.A. Caan
2020 arXiv   pre-print

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A pathology study was conducted by reconstructing simulated white matter lesions and prospectively undersampled data of a Multiple Sclerosis patient.  ...  Validation was performed against Compressed Sensing (CS) and further assessed based on data unseen during training.  ...  Simulating white matter lesions White matter lesions appear as hyperintensities in a 3D-FLAIR scan.  ... 
arXiv:2012.07819v1 fatcat:fa2aay2pm5cxddkm5i2jpbp4iq
Open Access
Citation

Dimitrios Karkalousos, Kai Lønning, Hanneke E. Hulst, Serge O. Dumoulin, Jan-Jakob Sonke, Frans M. Vos, Matthan W.A. Caan. "Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine." arXiv (2020)

Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets [article]

Reuben Dorent, Thomas Booth, Wenqi Li, Carole H. Sudre, Sina Kafiabadi, Jorge Cardoso, Sebastien Ourselin, Tom Vercauteren
2020 arXiv   pre-print

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The proposed framework is assessed on two different types of brain lesions: White matter lesions and gliomas.  ...  Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these  ...  Joint white matter lesion and tissue segmentation Task and datasets In this first set of experiments, we focus on the segmentation of white matter lesions and six tissue classes (white matter, grey matter  ... 
arXiv:2009.04009v1 fatcat:42c67fdsgvgh5n77upgqxrqlhu
Citation

Reuben Dorent, Thomas Booth, Wenqi Li, Carole H. Sudre, Sina Kafiabadi, Jorge Cardoso, Sebastien Ourselin, Tom Vercauteren. "Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets." arXiv (2020)

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