802 Hits in 14.5 sec

Neurodevelopmental Age Estimation of Infants Using a 3D-Convolutional Neural Network Model based on Fusion MRI Sequences [article]

M. Shabanian, A. Siddiqui, H. Chen, J.P. DeVincenzo
2020 arXiv   pre-print
We investigated a three-dimensional convolutional neural network (3D CNN) to rapidly classify brain developmental age using common MRI sequences.  ...  We developed a BDAE method using T1-weighted, as well as a fusion of T1-weighted, T2-weighted, and proton density (PD) sequences from 112 individual subjects using 3D CNN.  ...  Acknowledgments We would like to express our special thanks to the NIMH Data Archive for access to the NIH pediatric MRI dataset. We would also like to thank Dr. Courtney-Bricker and Ms.  ... 
arXiv:2010.03963v1 fatcat:tprn6h35ajfd5ilckq6xnctt44

Predicting brain-age from raw T 1 -weighted Magnetic Resonance Imaging data using 3D Convolutional Neural Networks [article]

Lukas Fisch, Jan Ernsting, Nils R. Winter, Vincent Holstein, Ramona Leenings, Marie Beisemann, Kelvin Sarink, Daniel Emden, Nils Opel, Ronny Redlich, Jonathan Repple, Dominik Grotegerd (+24 others)
2021 arXiv   pre-print
Age prediction based on Magnetic Resonance Imaging (MRI) data of the brain is a biomarker to quantify the progress of brain diseases and aging.  ...  Here we describe a 3D Convolutional Neural Network (CNN) based on the ResNet architecture being trained on raw, non-registered T 1 -weighted MRI data of N=10,691 samples from the German National Cohort  ...  The minimization of the MSE for the ANN using the preprocessed data is done by using the Adam algorithm (Kingma and Ba, 2015) with a learning rate of .01 as proposed in (Hahn et al., 2020) .  ... 
arXiv:2103.11695v1 fatcat:lm7wws4brbc7bp7pknk63snupu

Editorial: Deep Learning in Aging Neuroscience

Javier Ramírez, Juan M. Górriz, Andrés Ortiz, James H. Cole, Martin Dyrba
2020 Frontiers in Neuroinformatics  
Two papers of the research topic focused on brain age prediction by means of 18F-FDG brain metabolic topography data and structural T1-weighted MRI brain scans.  ...  The paper by Qureshi et al. showed an evaluation of functional decline in AD dementia using three-dimensional convolutional neural networks (3D-CNN) and group ICA to model functional connectivity from  ... 
doi:10.3389/fninf.2020.573974 fatcat:bkcjwyukzfdsxemx2zi7ch54jm

Predicting Dementia Severity by Merging Anatomical and Diffusion MRI with Deep 3D Convolutional Neural Networks [article]

Tamoghna Chattopadhyay, Amit Singh, Neha Ann Joshy, Sophia I Thomopoulos, Talia M. Nir, Hong Zheng, Elnaz Nourollahimoghadam, Umang Gupta, Greg Ver Steeg, Neda Jahanshad, Paul Thompson
2022 bioRxiv   pre-print
For benchmarking, we evaluate CNNs that use T1-weighted MRI and dMRI to estimate brain age - the task of predicting a persons chronological age from their neuroimaging data.  ...  We tested both 2D Slice CNN and 3D CNN neural network models for the above predictive tasks.  ...  BrainAge is typically predicted from T1-weighted brain images, but DTI-derived metrics can also be used for this prediction task.  ... 
doi:10.1101/2022.08.22.504801 fatcat:idla7bwyojfapiw7wrblni72au

Deep learning based prediction of Alzheimer's disease from magnetic resonance images [article]

Manu Subramoniam, Aparna T. R., Anurenjan P. R., Sreeni K. G
2021 arXiv   pre-print
In this paper, a deep neural network based prediction of AD from magnetic resonance images (MRI) is proposed.  ...  The state of the art image classification networks like VGG, residual networks (ResNet) etc. with transfer learning shows promising results.  ...  Convolutional neural networks (CNNs) using 3D T1-weighted images from the ADNI dataset is used by Silvia Basaia et al., Weiming Lin et al. [16] [17] .  ... 
arXiv:2101.04961v2 fatcat:k7paod67y5ezndm5ue27sik2ga

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Zhao Zhang, Guangfei Li, Yong Xu, Xiaoying Tang
2021 Diagnostics  
This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images.  ...  An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can  ...  In the radiological research of the human brain, the raw data is mainly the collected magnetic resonance images of the human brain.  ... 
doi:10.3390/diagnostics11081402 fatcat:mmouz5fb2ngzbe7jj2fyi5xpsy

Almost instant brain atlas segmentation for large-scale studies [article]

Alex Fedorov, Eswar Damaraju, Vince Calhoun, Sergey Plis
2017 arXiv   pre-print
Incredible speed due to available powerful GPUs neural network makes this analysis much easier and faster (from >10 hours to a minute).  ...  To alleviate these problems, we propose a feedforward fully convolutional neural network trained on the output produced by the state of the art models.  ...  Introduction Structural magnetic resonance imaging (sMRI) is an essential tool for the clinical care of patients providing details about the anatomical structure of the brain.  ... 
arXiv:1711.00457v1 fatcat:yhw6pv6lm5hwlcpfspmhxumzea

Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network

Pál Vakli, Regina J. Deák-Meszlényi, Tibor Auer, Zoltán Vidnyánszky
2020 Frontiers in Neuroinformatics  
We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about  ...  Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and  ...  ACKNOWLEDGMENTS This research has been conducted using the UK Biobank Resource under Application Number 27236.  ... 
doi:10.3389/fninf.2020.00010 pmid:32265681 pmcid:PMC7104804 fatcat:itza63l2xzdizf3w76q2vl37iy

Accurate brain age prediction with lightweight deep neural networks [article]

Han Peng, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, Stephen M Smith
2019 bioRxiv   pre-print
To address this, we propose a deep convolutional neural network model, Simple Fully Convolutional Network (SFCN), for accurate prediction of brain age using T1-weighted structural MRI data.  ...  It achieved state-of-the-art performance in UK Biobank data (N = 14,503), with mean absolute error (MAE) = 2.14y in brain age prediction and 99.3% in sex classification.  ...  Discussion To conclude, we proposed SFCN, a lightweight deep neural network architecture, which achieved state-of-the-art brain age prediction using T1-weighted structural MRI images.  ... 
doi:10.1101/2019.12.17.879346 fatcat:rve3rxf4fzbw7j5y3f3455bi2i

Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation

Yang Ding, Rolando Acosta, Vicente Enguix, Sabrina Suffren, Janosch Ortmann, David Luck, Jose Dolz, Gregory A. Lodygensky
2020 Frontiers in Neuroscience  
Deep learning neural networks are especially potent at dealing with structured data, such as images and volumes.  ...  Both networks were retrained over 24 pairs of neonatal T1 and T2 data from the Developing Human Connectome Project public data set and validated on another eight pairs against ground truth.  ...  As expected, both networks appear to benefit from the inclusion of T2-weighted images, potentially more so than from the inclusion of T1-weighted ones.  ... 
doi:10.3389/fnins.2020.00207 pmid:32273836 pmcid:PMC7114297 fatcat:eieffwq46rd47f73psw5s4tmz4

Brain Age Prediction of Children Using Routine Brain MR Images via Deep Learning

Jin Hong, Zhangzhi Feng, Shui-Hua Wang, Andrew Peet, Yu-Dong Zhang, Yu Sun, Ming Yang
2020 Frontiers in Neurology  
Traditional methods to estimate brain age based on 3D magnetic resonance (MR), T1 weighted imaging (T1WI), and diffusion tensor imaging (DTI) need complex preprocessing and extra scanning time, decreasing  ...  A 10-layer 3D convolutional neural network (CNN) was designed for predicting the brain age of children and it achieved reliable and accurate results on test data with a mean absolute deviation (MAE) of  ...  AUTHOR CONTRIBUTIONS ZF and MY collected the data. JH designed the algorithm. JH and Y-DZ preprocessed the data, designed the algorithm, and tested the model.  ... 
doi:10.3389/fneur.2020.584682 pmid:33193046 pmcid:PMC7604456 fatcat:u4tgdaezvva4jpq3jnhkedv6gu

Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI

Kaoutar Ben Ahmed, Lawrence O. Hall, Dmitry B. Goldgof, Robert Gatenby
2022 Diagnostics  
Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients.  ...  Additionally, multi-sequence MRI images were used to enhance survival prediction performance.  ...  Recent review works [10] show the domination of convolutional neural network techniques applied to brain magnetic resonance imaging (MRI) analysis compared to other approaches.  ... 
doi:10.3390/diagnostics12020345 pmid:35204436 pmcid:PMC8871067 fatcat:dzu456kgbjgszfumdsuwj6yxmi

Automatic Segmentation and Overall Survival Prediction in Gliomas using Fully Convolutional Neural Network and Texture Analysis [article]

Varghese Alex, Mohammed Safwan, Ganapathy Krishnamurthi
2017 arXiv   pre-print
In this paper, we use a fully convolutional neural network (FCNN) for the segmentation of gliomas from Magnetic Resonance Images (MRI).  ...  For the task of survival prediction, texture and shape based features were extracted from T1 post contrast volume to train an XGBoost regressor.  ...  Fig. 6 : 6 Reduction of False positive using connected components. (a) FLAIR. (b) Raw Prediction. (c) Post Processed image. (d) Ground Truth.  ... 
arXiv:1712.02066v1 fatcat:zcz4ngenlfcp7jmk5s5y3cqeli

Predicting Brain Age of Healthy Adults Based on Structural MRI Parcellation Using Convolutional Neural Networks

Huiting Jiang, Na Lu, Kewei Chen, Li Yao, Ke Li, Jiacai Zhang, Xiaojuan Guo
2020 Frontiers in Neurology  
In this study, we established age prediction models based on common structural networks using convolutional neural networks (CNN) with data from 1,454 healthy subjects aged 18-90 years.  ...  Structural magnetic resonance imaging (MRI) studies have demonstrated that the brain undergoes age-related neuroanatomical changes not only regionally but also on the network level during the normal development  ...  HJ and NL performed data analysis. HJ, KC, KL, JZ, and XG interpreted the results. HJ, NL, and XG drafted the manuscript.  ... 
doi:10.3389/fneur.2019.01346 pmid:31969858 pmcid:PMC6960113 fatcat:vg7y7lu5tvc5fcnlcwlt3m7erm

3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients [chapter]

Dong Nie, Han Zhang, Ehsan Adeli, Luyan Liu, Dinggang Shen
2016 Lecture Notes in Computer Science  
Specifically, we adopt 3D convolutional neural networks (CNNs) and also propose a new network architecture for using multi-channel data and learning supervised features.  ...  In this paper, we propose using deep learning frameworks to automatically extract features from multi-modal preoperative brain images (i.e., T1 MRI, fMRI and DTI) of high-grade glioma patients.  ...  Particularly, in the first step, we apply a supervised deep learning method, i.e., convolutional neural network (CNN) to extract high-level tumor appearance features from T1 MRI, fMRI and DTI images, for  ... 
doi:10.1007/978-3-319-46723-8_25 pmid:28149967 pmcid:PMC5278791 fatcat:p4lnh2t2freahkdc3pxrjo6p6q
« Previous Showing results 1 — 15 out of 802 results