Search

Your search keyword '"Juan Eugenio Iglesias"' showing total 86 results
Start Over Author "Juan Eugenio Iglesias" Publication Year Range Last 3 years
86 results on '"Juan Eugenio Iglesias"'

1. High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models

Author

Livia Rodrigues, Martina Bocchetta, Oula Puonti, Douglas Greve, Ana Carolina Londe, Marcondes França, Simone Appenzeller, Leticia Rittner, and Juan Eugenio Iglesias

Subjects
Science
Abstract

Abstract Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods. Recently, new techniques using synthetic images have emerged, reducing the need for manual annotation. Here we provide a dataset composed of label maps built from publicly available ultra-high resolution ex vivo MRI from 10 whole hemispheres, which can be used to develop segmentation methods using synthetic data. The label maps are obtained with a combination of manual labels for the hypothalamic regions and automated segmentations for the rest of the brain, and mirrored to simulate entire brains. We also provide the pre-processed ex vivo scans, as this dataset can support future projects to include other structures after these are manually segmented.

Published
2024
Full Text
View/download PDF

2. Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting

Author

Matthew J. Leming, Esther E. Bron, Rose Bruffaerts, Yangming Ou, Juan Eugenio Iglesias, Randy L. Gollub, and Hyungsoon Im

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Advances in artificial intelligence have cultivated a strong interest in developing and validating the clinical utilities of computer-aided diagnostic models. Machine learning for diagnostic neuroimaging has often been applied to detect psychological and neurological disorders, typically on small-scale datasets or data collected in a research setting. With the collection and collation of an ever-growing number of public datasets that researchers can freely access, much work has been done in adapting machine learning models to classify these neuroimages by diseases such as Alzheimer’s, ADHD, autism, bipolar disorder, and so on. These studies often come with the promise of being implemented clinically, but despite intense interest in this topic in the laboratory, limited progress has been made in clinical implementation. In this review, we analyze challenges specific to the clinical implementation of diagnostic AI models for neuroimaging data, looking at the differences between laboratory and clinical settings, the inherent limitations of diagnostic AI, and the different incentives and skill sets between research institutions, technology companies, and hospitals. These complexities need to be recognized in the translation of diagnostic AI for neuroimaging from the laboratory to the clinic.

Published
2023
Full Text
View/download PDF

3. A ready-to-use machine learning tool for symmetric multi-modality registration of brain MRI

Author

Juan Eugenio Iglesias

Subjects
Medicine, Science
Abstract

Abstract Volumetric registration of brain MRI is routinely used in human neuroimaging, e.g., to align different MRI modalities, to measure change in longitudinal analysis, to map an individual to a template, or in registration-based segmentation. Classical registration techniques based on numerical optimization have been very successful in this domain, and are implemented in widespread software suites like ANTs, Elastix, NiftyReg, or DARTEL. Over the last 7–8 years, learning-based techniques have emerged, which have a number of advantages like high computational efficiency, potential for higher accuracy, easy integration of supervision, and the ability to be part of a meta-architectures. However, their adoption in neuroimaging pipelines has so far been almost inexistent. Reasons include: lack of robustness to changes in MRI modality and resolution; lack of robust affine registration modules; lack of (guaranteed) symmetry; and, at a more practical level, the requirement of deep learning expertise that may be lacking at neuroimaging research sites. Here, we present EasyReg, an open-source, learning-based registration tool that can be easily used from the command line without any deep learning expertise or specific hardware. EasyReg combines the features of classical registration tools, the capabilities of modern deep learning methods, and the robustness to changes in MRI modality and resolution provided by our recent work in domain randomization. As a result, EasyReg is: fast; symmetric; diffeomorphic (and thus invertible); agnostic to MRI modality and resolution; compatible with affine and nonlinear registration; and does not require any preprocessing or parameter tuning. We present results on challenging registration tasks, showing that EasyReg is as accurate as classical methods when registering 1 mm isotropic scans within MRI modality, but much more accurate across modalities and resolutions. EasyReg is publicly available as part of FreeSurfer; see https://surfer.nmr.mgh.harvard.edu/fswiki/EasyReg .

Published
2023
Full Text
View/download PDF

4. Low‐Field Portable Magnetic Resonance Imaging for Post‐Thrombectomy Assessment of Ongoing Brain Injury

Author

Nanthiya Sujijantarat, Andrew B. Koo, Ivan Jambor, Ajay Malhotra, Mercy H. Mazurek, Nethra Parasuram, Vineetha Yadlapalli, Isha R. Chavva, Dheeraj Lalwani, Julia Zabinska, Joanna M. Roy, Joseph P. Antonios, Aladine A. Elsamadicy, Daniela Renedo, Ryan M. Hebert, Joseph L. Schindler, Emily J. Gilmore, Lauren H. Sansing, Adam de Havenon, Madelynne Olexa, Steven J. Schiff, Juan Eugenio Iglesias, Matthew Rosen, William Taylor Kimberly, Nils H. Petersen, Kevin N. Sheth, and Charles C. Matouk

Subjects
portable MRI, stroke, thrombectomy, Neurology. Diseases of the nervous system, RC346-429, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Background Timely imaging is essential for patients undergoing mechanical thrombectomy (MT). Our objective was to evaluate the safety and feasibility of low‐field portable magnetic resonance imaging (pMRI) for bedside evaluation following MT. Methods Patients with suspected large‐vessel occlusion undergoing MT were screened for eligibility. All pMRI examinations were conducted in the standard ferromagnetic environment of the interventional radiology suite. Clinical characteristics, procedural details, and pMRI features were collected. Subsequent high‐field conventional MRI within 72±12 hours was analyzed. If a conventional MRI was not available for comparison, computed tomography within the same time frame was used for validation. Results Twenty‐four patients were included (63% women; median age, 76 years [interquartile range, 69–84 years]). MT was performed with a median access to revascularization time of 15 minutes (interquartile range, 8–19 minutes), and with a successful outcome as defined by a thrombolysis in cerebral infarction score of ≥2B in 90% of patients. The median time from the end of the procedure to pMRI was 22 minutes (interquartile range, 16–32 minutes). The median pMRI examination time was 30 minutes (interquartile range, 17–33 minutes). Of 23 patients with available subsequent imaging, 9 had infarct progression compared with immediate post‐MT pMRI and 14 patients did not have progression of their infarct volume. There was no adverse event related to the examination. Conclusion Low‐field pMRI is safe and feasible in a post‐MT environment and enables timely identification of ischemic changes in the interventional radiology suite. This approach can facilitate the assessment of baseline infarct burden and may help guide physiological interventions following MT.

Published
2023
Full Text
View/download PDF

5. Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

Author

Henry F.J. Tregidgo, Sonja Soskic, Juri Althonayan, Chiara Maffei, Koen Van Leemput, Polina Golland, Ricardo Insausti, Garikoitz Lerma-Usabiaga, César Caballero-Gaudes, Pedro M. Paz-Alonso, Anastasia Yendiki, Daniel C. Alexander, Martina Bocchetta, Jonathan D. Rohrer, and Juan Eugenio Iglesias

Subjects
Thalamus, Atlasing, Diffusion MRI, Segmentation, Bayesian inference, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).

Published
2023
Full Text
View/download PDF

6. Volume of hyperintense inflammation (VHI): A quantitative imaging biomarker of inflammation load in spondyloarthritis, enabled by human-machine cooperation.

Author

Carolyna Hepburn, Alexis Jones, Alan Bainbridge, Coziana Ciurtin, Juan Eugenio Iglesias, Hui Zhang, Margaret A Hall-Craggs, and Timothy J P Bray

Subjects
Medicine, Science
Abstract

Qualitative visual assessment of MRI scans is a key mechanism by which inflammation is assessed in clinical practice. For example, in axial spondyloarthritis (axSpA), visual assessment focuses on the identification of regions with increased signal in the bone marrow, known as bone marrow oedema (BMO), on water-sensitive images. The identification of BMO has an important role in the diagnosis, quantification and monitoring of disease in axSpA. However, BMO evaluation depends heavily on the experience and expertise of the image reader, creating substantial imprecision. Deep learning-based segmentation is a natural approach to addressing this imprecision, but purely automated solutions require large training sets that are not currently available, and deep learning solutions with limited data may not be sufficiently trustworthy for use in clinical practice. To address this, we propose a workflow for inflammation segmentation incorporating both deep learning and human input. With this 'human-machine cooperation' workflow, a preliminary segmentation is generated automatically by deep learning; a human reader then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which is proposed as a quantitative imaging biomarker (QIB) of inflammation load in axSpA. We implemented and evaluated the proposed human-machine workflow in a cohort of 29 patients with axSpA who had undergone prospective MRI scans before and after starting biologic therapy. The performance of the workflow was compared against purely visual assessment in terms of inter-observer/inter-method segmentation overlap, inter-observer agreement and assessment of response to biologic therapy. The human-machine workflow showed superior inter-observer segmentation overlap than purely manual segmentation (Dice score 0.84 versus 0.56). VHI measurements produced by the workflow showed similar or better inter-observer agreement than visual scoring, with similar response assessments. We conclude that the proposed human-machine workflow offers a mechanism to improve the consistency of inflammation assessment, and that VHI could be a valuable QIB of inflammation load in axSpA, as well as offering an exemplar of human-machine cooperation more broadly.

Published
2023
Full Text
View/download PDF

7. High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning

Author

Adrià Casamitjana and Juan Eugenio Iglesias

Subjects
Subcortex, MRI, Segmentation, Atlas, Machine learning, Survey, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.

Published
2022
Full Text
View/download PDF

8. A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images

Author

Douglas N. Greve, Benjamin Billot, Devani Cordero, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Bruce Fischl, Juan Eugenio Iglesias, and Jean C. Augustinack

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need as there are few, if any, publicly available tools to segment these clinically relevant structures. A U-Net with spatial, intensity, contrast, and noise augmentation was trained using 39 manually labeled MRI data sets. In general, the Dice scores, true positive rates, false discovery rates, and manual-automatic volume correlation were very good relative to comparable tools for other structures. A diverse data set of 698 subjects were segmented using the tool; evaluation of the resulting labelings showed that the tool failed in less than 1% of cases. Test-retest reliability of the tool was excellent. The automatically segmented volume of all structures except mammillary bodies showed effectiveness at detecting either clinical AD effects, age effects, or both. This tool will be publicly released with FreeSurfer (surfer.nmr.mgh.harvard.edu/fswiki/ScLimbic). Together with the other cortical and subcortical limbic segmentations, this tool will allow FreeSurfer to provide a comprehensive view of the limbic system in an automated way.

Published
2021
Full Text
View/download PDF

9. Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution

Author

Chao J. Liu, William Ammon, Viviana Siless, Morgan Fogarty, Ruopeng Wang, Alessia Atzeni, Iman Aganj, Juan Eugenio Iglesias, Lilla Zöllei, Bruce Fischl, Jeremy D. Schmahmann, and Hui Wang

Subjects
Cerebellum, Polarization-sensitive optical coherence tomography, Layer thickness, Cell density, Myelin, Cross-subject variability, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases.

Published
2021
Full Text
View/download PDF

10. Quantifying White Matter Hyperintensity and Brain Volumes in Heterogeneous Clinical and Low-Field Portable MRI.

Author

Pablo Laso, Stefano Cerri, Annabel Sorby-Adams, Jennifer Guo, Farrah Mateen, Philipp Goebl, Jiaming Wu, Peirong Liu, Hongwei Bran Li, Sean I. Young, Benjamin Billot, Oula Puonti, Gordon Sze, Sam Payabavash, Adam DeHavenon, Kevin N. Sheth, Matthew S. Rosen, John Kirsch, Nicola Strisciuglio, Jelmer M. Wolterink, Arman Eshaghi, Frederik Barkhof, W. Taylor Kimberly, and Juan Eugenio Iglesias

Published
2024
Full Text
View/download PDF

13. BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023.

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar, Zhifan Jiang, Anna Zapaishchykova, Julija Pavaine, Lubdha M. Shah, Blaise V. Jones, Nakul Sheth, Sanjay P. Prabhu, Aaron S. McAllister, Wenxin Tu, Khanak K. Nandolia, Andres Felipe Rodriguez, Ibraheem Salman Shaikh, Mariana Sanchez-Montano, Hollie Anne Lai, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Hannah Anderson, Syed Muhammed Anwar, Alejandro Aristizabal, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Austin J. Borja, Evan Calabrese, Verena Chung, Gian Marco Conte, James A. Eddy, Ivan Ezhov, Ariana M. Familiar, Keyvan Farahani, Deep Gandhi, Anurag Gottipati, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Elaine, Alexandros Karargyris, Hasan Kassem, Neda Khalili, Florian Kofler, Dominic LaBella, Koen Van Leemput, Hongwei Li 0004, Nazanin Maleki, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Sarthak Pati, Marie Piraud, Tina Poussaint, Zachary J. Reitman, Jeffrey D. Rudie, Rachit Saluja, Micah J. Sheller, Russell Takeshi Shinohara, Karthik Viswanathan, Chunhao Wang, Benedikt Wiestler, Walter F. Wiggins, Christos Davatzikos, Phillip B. Storm, Miriam Bornhorst, Roger J. Packer, Trent Hummel, Peter de Blank, Lindsey Hoffman, Mariam Aboian, Ali Nabavizadeh, Jeffrey B. Ware, Benjamin H. Kann, Brian Rood, Adam C. Resnick, Spyridon Bakas, Arastoo Vossough, and Marius George Linguraru

Published
2024
Full Text
View/download PDF

14. Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge.

Author

Dominic LaBella, Ujjwal Baid, Omaditya Khanna, Shan McBurney-Lin, Ryan McLean, Pierre Nedelec, Arif Rashid, Nourel Hoda Tahon, Talissa Altes, Radhika Bhalerao, Yaseen Dhemesh, Devon Godfrey, Fathi Hilal, Scott Floyd, Anastasia Janas, Anahita Fathi Kazerooni, John Kirkpatrick, Collin Kent, Florian Kofler, Kevin Leu, Nazanin Maleki, Bjoern H. Menze, Maxence Pajot, Zachary J. Reitman, Jeffrey D. Rudie, Rachit Saluja, Yury Velichko, Chunhao Wang, Pranav Warman, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Syed Muhammad Anwar, Timothy Bergquist, Sully Chen, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Nastaran Khalili, Juan Eugenio Iglesias, Zhifan Jiang, Elaine Johanson, Koen Van Leemput, Hongwei Bran Li, Marius George Linguraru, Xinyang Liu, Aria Mahtabfar, Zeke Meier, Ahmed W. Moawad, John Mongan, Marie Piraud, Russell Takeshi Shinohara, Walter F. Wiggins, Aly H. Abayazeed, Rachel Akinola, András Jakab, Michel Bilello, Maria Correia de Verdier, Priscila Crivellaro, Christos Davatzikos, Keyvan Farahani, John B. Freymann, Christopher Hess, Raymond Y. Huang, Philipp Lohmann, Mana Moassefi, Matthew W. Pease, Philipp Vollmuth, Nico Sollmann, David Diffley, Khanak K. Nandolia, Daniel I. Warren, Ali Hussain, Pascal Fehringer, Yulia Bronstein, Lisa Deptula, Evan G. Stein, Mahsa Taherzadeh, Eduardo Portela de Oliveira, Aoife Haughey, Marinos Kontzialis, Luca Saba, Benjamin Turner, Melanie M. T. Brüßeler, Shehbaz Ansari, Athanasios Gkampenis, David Maximilian Weiss, Aya Mansour, Islam H. Shawali, Nikolay Yordanov, Joel M. Stein, Roula Hourani, Mohammed Yahya Moshebah, Ahmed Magdy Abouelatta, Tanvir Rizvi, Klara Willms, Dann C. Martin, and Abdullah Okar

Published
2024
Full Text
View/download PDF

19. The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI.

Author

Maria Correia de Verdier, Rachit Saluja, Louis Gagnon, Dominic LaBella, Ujjwal Baid, Nourel Hoda Tahon, Martha Foltyn-Dumitru, Jikai Zhang, Maram Alafif, Saif Baig, Ken Chang, Gennaro D'Anna, Lisa Deptula, Diviya Gupta, Muhammad Ammar Haider, Ali Hussain, Michael Iv, Marinos Kontzialis, Paul Manning, Farzan Moodi, Teresa Nunes, Aaron Simon, Nico Sollmann, David Vu, Maruf Adewole, Jake Albrecht, Udunna Anazodo, Rongrong Chai, Verena Chung, Shahriar Faghani, Keyvan Farahani, Anahita Fathi Kazerooni, Juan Eugenio Iglesias, Florian Kofler, Hongwei Li 0004, Marius George Linguraru, Bjoern H. Menze, Ahmed W. Moawad, Yury Velichko, Benedikt Wiestler, Talissa Altes, Patil Basavasagar, Martin Bendszus, Gianluca Brugnara, Jaeyoung Cho, Yaseen Dhemesh, Brandon K. K. Fields, Filip Garrett, Jaime Gass, Lubomir M. Hadjiiski, Jona A. Hattangadi-Gluth, Christopher Hess, Jessica L. Houk, Edvin Isufi, Lester J. Layfield, George Mastorakos, John Mongan, Pierre Nedelec, Uyen Nguyen, Sebastian Oliva, Matthew W. Pease, Aditya Rastogi, Jason Sinclair, Robert X. Smith, Leo P. Sugrue, Jonathan Thacker, Igor Vidic, Javier E. Villanueva-Meyer, Nathan S. White, Mariam Aboian, Gian Marco Conte, Anders M. Dale, Mert R. Sabuncu, Tyler M. Seibert, Brent Weinberg, Aly H. Abayazeed, Raymond Y. Huang, Sevcan Turk, Andreas M. Rauschecker, Nikdokht Farid, Philipp Vollmuth, Ayman Nada, Spyridon Bakas, Evan Calabrese, and Jeffrey D. Rudie

Published
2024
Full Text
View/download PDF

20. The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Deep Gandhi, Xinyang Liu, Zhifan Jiang, Syed Muhammed Anwar, Jake Albrecht, Maruf Adewole, Udunna Anazodo, Hannah Anderson, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Austin J. Borja, Evan Calabrese, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Ariana Familiar, Keyvan Farahani, Anurag Gottipati, Debanjan Haldar, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Johansen, Blaise V. Jones, Neda Khalili, Florian Kofler, Dominic LaBella, Hollie Anne Lai, Koen Van Leemput, Hongwei Bran Li, Nazanin Maleki, Aaron S. McAllister, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Khanak K. Nandolia, Julija Pavaine, Marie Piraud, Tina Poussaint, Sanjay P. Prabhu, Zachary Reitman, Andres Felipe Rodriguez, Jeffrey D. Rudie, Mariana Sanchez-Montano, Ibraheem Salman Shaikh, Lubdha M. Shah, Nakul Sheth, Russel Taki Shinohara, Wenxin Tu, Karthik Viswanathan, Chunhao Wang, Jeffrey B. Ware, Benedikt Wiestler, Walter I. Wiggins, Anna Zapaishchykova, Mariam Aboian, Miriam Bornhorst, Peter de Blank, Michelle Deutsch, Maryam Fouladi, Lindsey Hoffman, Benjamin H. Kann, Margot Lazow, Leonie Mikael, Ali Nabavizadeh, Roger J. Packer, Spyridon Bakas, Adam C. Resnick, Brian Rood, Arastoo Vossough, and Marius George Linguraru

Published
2024
Full Text
View/download PDF

32. How Machine Learning is Powering Neuroimaging to Improve Brain Health.

Author

Nalini M. Singh, Jordan B. Harrod, Sandya Subramanian, Mitchell B. Robinson, Ken Chang, Suheyla Cetin Karayumak, Adrian Vasile Dalca, Simon B. Eickhoff, Michael Fox, Loraine Franke, Polina Golland, Daniel Haehn, Juan Eugenio Iglesias, Lauren J. O'Donnell, Yangming Ou, Yogesh Rathi, Shan H. Siddiqi, Haoqi Sun, M. Brandon Westover, Susan L. Whitfield-Gabrieli, and Randy L. Gollub

Published
2022
Full Text
View/download PDF

36. The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs).

Author

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu, Debanjan Haldar, Zhifan Jiang, Syed Muhammad Anwar, Jake Albrecht, Maruf Adewole, Udunna Anazodo, Hannah Anderson, Sina Bagheri, Ujjwal Baid, Timothy Bergquist, Evan Calabrese, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Ariana Familiar, Keyvan Farahani, Shuvanjan Haldar, Juan Eugenio Iglesias, Anastasia Janas, Elaine Johanson, Florian Kofler, Dominic LaBella, Koen Van Leemput, Hongwei Bran Li, Nazanin Maleki, Zeke Meier, Bjoern H. Menze, Ahmed W. Moawad, Marie Piraud, Tina Poussaint, Zachary Reitman, Jeffrey D. Rudie, Ibraheem Salman Shaikh, Russell Taki Shinohara, Wenxin Tu, Karthik Viswanathan, Chunhao Wang, Jeffrey B. Ware, Benedikt Wiestler, Walter I. Wiggins, Anna Zapaishchykova, Mariam Aboian, Miriam Bornhorst, Peter de Blank, Michelle Deutsch, Maryam Fouladi, Lindsey Hoffman, Benjamin H. Kann, Margot Lazow, Leonie Mikael, Ali Nabavizadeh, Roger J. Packer, Adam C. Resnick, Brian Rood, Arastoo Vossough, Spyridon Bakas, and Marius George Linguraru

Published
2023
Full Text
View/download PDF

38. The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Author

Dominic LaBella, Maruf Adewole, Michelle Alonso-Basanta, Talissa Altes, Syed Muhammad Anwar, Ujjwal Baid, Timothy Bergquist, Radhika Bhalerao, Sully Chen, Verena Chung, Gian Marco Conte, Farouk Dako, James A. Eddy, Ivan Ezhov, Devon Godfrey, Fathi Hilal, Ariana Familiar, Keyvan Farahani, Juan Eugenio Iglesias, Zhifan Jiang, Elaine Johanson, Anahita Fathi Kazerooni, Collin Kent, John Kirkpatrick, Florian Kofler, Koen Van Leemput, Hongwei Bran Li, Xinyang Liu, Aria Mahtabfar, Shan McBurney-Lin, Ryan McLean, Zeke Meier, Ahmed W. Moawad, John Mongan, Pierre Nedelec, Maxence Pajot, Marie Piraud, Arif Rashid, Zachary Reitman, Russell Takeshi Shinohara, Yury Velichko, Chunhao Wang, Pranav I. Warman, Walter I. Wiggins, Mariam Aboian, Jake Albrecht, Udunna Anazodo, Spyridon Bakas, Adam E. Flanders, Anastasia Janas, Goldey Khanna, Marius George Linguraru, Bjoern H. Menze, Ayman Nada, Andreas M. Rauschecker, Jeffrey D. Rudie, Nourel Hoda Tahon, Javier E. Villanueva-Meyer, Benedikt Wiestler, and Evan Calabrese

Published
2023
Full Text
View/download PDF

40. The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Author

Hongwei Bran Li, Gian Marco Conte, Syed Muhammad Anwar, Florian Kofler, Koen Van Leemput, Marie Piraud, Ivan Ezhov, Felix Meissen, Maruf Adewole, Anastasia Janas, Anahita Fathi Kazerooni, Dominic LaBella, Ahmed W. Moawad, Keyvan Farahani, James A. Eddy, Timothy Bergquist, Verena Chung, Russell Takeshi Shinohara, Farouk Dako, Walter I. Wiggins, Zachary Reitman, Chunhao Wang, Xinyang Liu, Zhifan Jiang, Ariana Familiar, Elaine Johanson, Zeke Meier, Christos Davatzikos, John B. Freymann, Justin S. Kirby, Michel Bilello, Hassan M. Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Rivka R. Colen, Aikaterini Kotrotsou, Pamela LaMontagne, Daniel S. Marcus, Mikhail Milchenko, Arash Nazeri, Marc-André Weber, Abhishek Mahajan, Suyash Mohan, John Mongan, Christopher Hess, Soonmee Cha, Javier E. Villanueva-Meyer, Errol Colak, Priscila Crivellaro, András Jakab, Jake Albrecht, Udunna Anazodo, Mariam Aboian, Thomas Yu, Ujjwal Baid, Spyridon Bakas, Marius George Linguraru, Bjoern H. Menze, Juan Eugenio Iglesias, and Benedikt Wiestler

Published
2023
Full Text
View/download PDF

41. Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI.

Author

Pablo Laso, Stefano Cerri, Annabel Sorby-Adams, Jennifer Guo, Farrah Mateen, Philipp Goebl, Jiaming Wu, Peirong Liu, Hongwei Li 0004, Sean I. Young, Benjamin Billot, Oula Puonti, Gordon Sze, Sam Payabavash, Adam DeHavenon, Kevin N. Sheth, Matthew S. Rosen, John Kirsch, Nicola Strisciuglio, Jelmer M. Wolterink, Arman Eshaghi, Frederik Barkhof, W. Taylor Kimberly, and Juan Eugenio Iglesias

Published
2023
Full Text
View/download PDF

42. The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa).

Author

Maruf Adewole, Jeffrey D. Rudie, Anu Gbadamosi, Oluyemisi Toyobo, Raymond Confidence, Dong Zhang, Olubukola Omidiji, Rachel Akinola, Mohammad Abba Suwaid, Adaobi Emegoakor, Nancy Ojo, Kenneth Aguh, Chinasa Kalaiwo, Gabriel Babatunde, Afolabi Ogunleye, Yewande Gbadamosi, Kator Iorpagher, Evan Calabrese, Mariam Aboian, Marius George Linguraru, Jake Albrecht, Benedikt Wiestler, Florian Kofler, Anastasia Janas, Dominic LaBella, Anahita Fathi Kazerooni, Hongwei Bran Li, Juan Eugenio Iglesias, Keyvan Farahani, James A. Eddy, Timothy Bergquist, Verena Chung, Russell Takeshi Shinohara, Walter I. Wiggins, Zachary Reitman, Chunhao Wang, Xinyang Liu, Zhifan Jiang, Ariana Familiar, Koen Van Leemput, Christina Bukas, Marie Piraud, Gian Marco Conte, Elaine Johanson, Zeke Meier, Bjoern H. Menze, Ujjwal Baid, Spyridon Bakas, Farouk Dako, Abiodun Fatade, and Udunna Anazodo

Published
2023
Full Text
View/download PDF

43. The Brain Tumor Segmentation (BraTS) Challenge 2023: Local Synthesis of Healthy Brain Tissue via Inpainting.

Author

Florian Kofler, Felix Meissen, Felix Steinbauer, Robert Graf, Eva Oswald, Ezequiel de la Rosa, Hongwei Bran Li, Ujjwal Baid, Florian A. Hölzl, özgün Turgut, Izabela Horvath, Diana Waldmannstetter, Christina Bukas, Maruf Adewole, Syed Muhammad Anwar, Anastasia Janas, Anahita Fathi Kazerooni, Dominic LaBella, Ahmed W. Moawad, Keyvan Farahani, James A. Eddy, Timothy Bergquist, Verena Chung, Russell Takeshi Shinohara, Farouk Dako, Walter I. Wiggins, Zachary Reitman, Chunhao Wang, Xinyang Liu, Zhifan Jiang, Ariana Familiar, Gian Marco Conte, Elaine Johanson, Zeke Meier, Christos Davatzikos, John B. Freymann, Justin S. Kirby, Michel Bilello, Hassan M. Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Rivka R. Colen, Aikaterini Kotrotsou, Pamela LaMontagne, Daniel S. Marcus, Mikhail Milchenko, Arash Nazeri, Marc-André Weber, Abhishek Mahajan, Suyash Mohan, John Mongan, Christopher Hess, Soonmee Cha, Javier E. Villanueva-Meyer, Errol Colak, Priscila Crivellaro, András Jakab, Jake Albrecht, Udunna Anazodo, Mariam Aboian, Juan Eugenio Iglesias, Koen Van Leemput, Spyridon Bakas, Daniel Rueckert, Benedikt Wiestler, Ivan Ezhov, Marie Piraud, and Bjoern H. Menze

Published
2023
Full Text
View/download PDF

46. Accurate super-resolution low-field brain MRI.

Author

Juan Eugenio Iglesias, Riana Schleicher, Sonia Laguna, Benjamin Billot, Pamela Schaefer, Brenna McKaig, Joshua N. Goldstein, Kevin N. Sheth, Matthew S. Rosen, and W. Taylor Kimberly

Published
2022

49. Future of Neurology & Technology: Neuroimaging Made Accessible Using Low-Field, Portable MRI

Author

Nethra R. Parasuram, Anna L. Crawford, Mercy H. Mazurek, Isha R. Chavva, Rachel Beekman, Emily J. Gilmore, Nils H. Petersen, Seyedmehdi Payabvash, Gordon Sze, Juan Eugenio Iglesias, Sacit B. Omay, Charles Matouk, Erin E. Longbrake, Adam de Havenon, Steven J. Schiff, Matthew S. Rosen, W. Taylor Kimberly, and Kevin N. Sheth

Subjects
Neurology (clinical)
Published
2023

Catalog

Books, media, physical & digital resources
See catalog results