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Harmonized Segmentation of Neonatal Brain MRI

Irina Grigorescu, Lucy Vanes, Alena Uus, Dafnis Batalle, Lucilio Cordero-Grande, Chiara Nosarti, A. David Edwards, Joseph V. Hajnal, Marc Modat, Maria Deprez
2021 Frontiers in Neuroscience  
We compare the two methods with a baseline fully-supervised segmentation network and report our results in terms of Dice scores obtained on our source test dataset.  ...  In this work, we aim to predict tissue segmentation maps on T2-weighted magnetic resonance imaging data of an unseen preterm-born neonatal population, which has both different acquisition parameters and  ...  This paper is an extension of our previous work (Grigorescu et al., 2020) .  ... 
doi:10.3389/fnins.2021.662005 pmid:34121991 pmcid:PMC8195278 fatcat:o3a3roxusbfpvk7ibftoyxwgmq

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

Liying Peng, Lanfen Lin, Yusen Lin, Yen-wei Chen, Zhanhao Mo, Roza M. Vlasova, Sun Hyung Kim, Alan C. Evans, Stephen R. Dager, Annette M. Estes, Robert C. McKinstry, Kelly N. Botteron (+9 others)
2021 Frontiers in Neuroscience  
In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life.  ...  The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018).  ...  The Infant Brain Imaging Study (IBIS) Network is an NIH funded Autism Centers of Excellence project and consists of a consortium of 9 universities in the U.S. and Canada.  ... 
doi:10.3389/fnins.2021.653213 pmid:34566556 pmcid:PMC8458966 fatcat:lrlausbt5zgttjwuwsonpu5p6y

Towards robust and generalizable super-resolution generative adversarial networks for magnetic resonance neuroimaging: a cross-population approach [article]

Leona Charlotte Förster, Lucas da Costa Campos, Martin Kocher, Svenja Caspers
2022 bioRxiv   pre-print
We investigated the generalization abilities of Super Resolution Generative Adversarial Neural Networks (SRGANs) across different populations.  ...  AbstractMagnetic resonance imaging (MRI) is fundamental to neuroscience, where detailed structural brain scans improve clinical diagnoses and provide accurate neuroanatomical information.  ...  in classification 9 , segmentation 10 , and image generation [11] [12] [13] .  ... 
doi:10.1101/2022.06.13.495858 fatcat:7at2gretd5achaakyvdsq2hj2y

Deep Learning in the Biomedical Applications: Recent and Future Status

Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu
2019 Applied Sciences  
), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM).  ...  Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain.  ...  Generative adversarial networks are also used in medical imaging segmentation.  ... 
doi:10.3390/app9081526 fatcat:srjvngtufbhstfcvn4mvhmrdve

Generative Adversarial Registration for Improved Conditional Deformable Templates [article]

Neel Dey, Mengwei Ren, Adrian V. Dalca, Guido Gerig
2022 arXiv   pre-print
We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework  ...  These improvements enable more accurate population modeling with diverse covariates for standardized downstream analyses and easier anatomical delineation for structures of interest.  ...  The resulting templates are sharp and easy to delineate for domain-experts, are more representative of the underlying demographics, and closely follow typical development in both neonatal MRI of developing  ... 
arXiv:2105.04349v2 fatcat:lg32vthf6nchlaom3igzpfnvmi

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
We also outline the limitations of existing techniques and discuss potential directions for future research.  ...  As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  To improve perceptual quality, the GCN is employed as the generator in a generative adversarial network.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

A Deep Generative Model of Neonatal Cortical Surface Development [article]

Abdulah Fawaz, Logan Z. Williams, A. David Edwards, Emma Robinson
2022 arXiv   pre-print
Deep Generative models have the potential to lead to clinically interpretable models of disease, but developing these on the cortical surface is challenging since established techniques for learning convolutional  ...  The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes.  ...  Introduction Deep Generative modelling presents enormous opportunities for medical imaging analysis: from image segmentation [13, 9, 40, 8] , registration [39] , motion correction and denoising [30,  ... 
arXiv:2206.07542v2 fatcat:fomorbcqo5eqri3xf3rzrj53gm

2020 Index IEEE Transactions on Biomedical Engineering Vol. 67

2020 IEEE Transactions on Biomedical Engineering  
Miniaturization Effects and Channel Selection Strategies for EEG Sensor Networks With Application to Auditory Attention Detection; TBME Jan. 2020 234-244 Natarajan, K., see Chandrasekhar, A., TBME Nov  ...  Chiang, K., and Jung, T., Facilitating Calibration in High-Speed BCI Spellers via Leveraging Cross-Device Shared Latent Responses; TBME April 2020 1105-1113 Narayanan, A.M., and Bertrand, A., Analysis of  ...  ., +, TBME March 2020 915-923 Image Quality Improvement of Hand-Held Ultrasound Devices With a Two-Stage Generative Adversarial Network.  ... 
doi:10.1109/tbme.2020.3048339 fatcat:y7zxxew27fgerapsnrhh54tm7y

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

Juan Eugenio Iglesias, Benjamin Billot, Yaël Balbastre, Azadeh Tabari, John Conklin, R. Gilberto González, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl
2021 NeuroImage  
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints  ...  We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based  ...  The collection and sharing of the MRI data used in the group study based on ADNI was funded by the Alzheimer's Disease Neuroimaging Initiative ( NIH grant U01-AG024904 ) and DOD ADNI ( Department of Defence  ... 
doi:10.1016/j.neuroimage.2021.118206 pmid:34048902 fatcat:3m7giodjvbhrhd5v6ngucz7ola

An Overview of Deep Learning Techniques for Epileptic Seizures Detection and Prediction Based on Neuroimaging Modalities: Methods, Challenges, and Future Works [article]

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Parisa Moridian, Roohallah Alizadehsani, Yinan Kong, Juan Manuel Gorriz, Javier Ramírez, Abbas Khosravi, Saeid Nahavandi, U. Rajendra Acharya
2022 arXiv   pre-print
Since epilepsy happens due to abnormal activity in the brain, seizures can affect any process your brain handles.  ...  Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), implantable, cloud computing, internet of things (IoT), hardware implementation of DL techniques  ...  Figure 7 shows a general form of a 2D-CNN used for epileptic seizure detection. B. Generative Adversarial Networks (GANs) In 2014, Goodfellow et al.  ... 
arXiv:2105.14278v2 fatcat:dxv3nkbyajetjokhf5eitttpgi

Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods

Shruti Atul Mali, Abdalla Ibrahim, Henry C. Woodruff, Vincent Andrearczyk, Henning Müller, Sergey Primakov, Zohaib Salahuddin, Avishek Chatterjee, Philippe Lambin
2021 Journal of Personalized Medicine  
We also reflect upon the importance of deep learning solutions for addressing variability across multi-centric radiomic studies especially using generative adversarial networks (GANs), neural style transfer  ...  Radiomics converts medical images into mineable data via a high-throughput extraction of quantitative features used for clinical decision support.  ...  GANs consist of two adversarial networks, a generator that generates realistic data and a discriminator that distinguishes whether the data is real or fake.  ... 
doi:10.3390/jpm11090842 pmid:34575619 fatcat:2ngorzmaw5alrpj7deecvvf4au

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  Mengoudi, K., +, JBHI Nov. 2020 3066-3075 Generative adversarial networks Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning.  ...  ., +, JBHI Jan. 2020 39-49 Dehaze of Cataractous Retinal Images Using an Unpaired Generative Adversarial Network.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

Learning Neural Textual Representations for Citation Recommendation

Binh Thanh Kieu, Inigo Jauregi Unanue, Son Bao Pham, Hieu Xuan Phan, Massimo Piccardi
2021 2020 25th International Conference on Pattern Recognition (ICPR)  
Danijel 1532 End-To-End Training of a Two-Stage Neural Network for Defect Detection GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks Deep transformation  ...  Yongpei; Zhou, Zicong; Liao, Guojun; Yuan, Kehong 1555 BCAU-Net: A Novel Architecture with Binary Channel Attention Module for MRI Brain Segmentation DAY 3 -Jan 14, 2021 Wang, Li-Wen; Siu, Wan-Chi  ... 
doi:10.1109/icpr48806.2021.9412725 fatcat:3vge2tpd2zf7jcv5btcixnaikm

Towards Clinical Application of Artificial Intelligence in Ultrasound Imaging

Masaaki Komatsu, Akira Sakai, Ai Dozen, Kanto Shozu, Suguru Yasutomi, Hidenori Machino, Ken Asada, Syuzo Kaneko, Ryuji Hamamoto
2021 Biomedicines  
We also discuss US image preprocessing, ingenious algorithms that are suitable for US imaging analysis, AI explainability for obtaining informed consent, the approval process of medical AI devices, and  ...  Ultrasound (US) imaging is commonly used in an extensive range of medical fields.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/biomedicines9070720 fatcat:aj5jsjjglbhfnhhzslnkp5zahy
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