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2021 IEEE Transactions on Biomedical Engineering  
Zhang 3417 Rapid Estimation of Entire Brain Strain Using Deep Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . X. Zhan, Y. Liu, S. J. Raymond, H. V. Alizadeh, A. G. Domel, O.  ...  Iordachita 3356 Time-Resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V.  ... 
doi:10.1109/tbme.2021.3115222 fatcat:i53ke5tqebc7lg6o6a6fvtlqvq

Data-driven Uncertainty Quantification in Computational Human Head Models [article]

Kshitiz Upadhyay, Dimitris G. Giovanis, Ahmed Alshareef, Andrew K. Knutsen, Curtis L. Johnson, Aaron Carass, Philip V. Bayly, Michael D. Shields, K.T. Ramesh
2022 arXiv   pre-print
UQ of strain fields highlight significant spatial variation in model uncertainty, and reveal key differences in uncertainty among commonly used strain-based brain injury predictor variables.  ...  Computational models of the human head are promising tools for estimating the impact-induced response of brain, and thus play an important role in the prediction of traumatic brain injury.  ...  Acknowledgements This research was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health (NIH) under Grant No. U01 NS11212.  ... 
arXiv:2110.15553v2 fatcat:omu2nq7bnjhbhoedvke4p6iw2u

Deep Learning in Ultrasound Elastography Imaging [article]

Hongliang Li, Manish Bhatt, Zhen Qu, Shiming Zhang, Martin C. Hartel, Ali Khademhosseini, Guy Cloutier
2020 arXiv   pre-print
Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis.  ...  Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected.  ...  Conflict of Interest The authors declare no conflict of interest.  ... 
arXiv:2010.07360v2 fatcat:ddax2defkfaz5jj6wpsplb4evq

Automatic Skull Stripping of Rat and Mouse Brain MRI Data Using U-Net

Li-Ming Hsu, Shuai Wang, Paridhi Ranadive, Woomi Ban, Tzu-Hao Harry Chao, Sheng Song, Domenic Hayden Cerri, Lindsay R. Walton, Margaret A. Broadwater, Sung-Ho Lee, Dinggang Shen, Yen-Yu Ian Shih
2020 Frontiers in Neuroscience  
In this study, we proposed a deep-learning-based framework, U-Net, to automatically identify the rodent brain boundaries in MR images.  ...  To benchmark the efficiency of this method, we trained and validated our model using both in-house collected and publicly available datasets.  ...  Here we propose a novel model that adopts a fully convolutional deep-learning network, U-Net (Ronneberger et al., 2015; Yogananda et al., 2019) , to perform dense feature extraction.  ... 
doi:10.3389/fnins.2020.568614 pmid:33117118 pmcid:PMC7575753 fatcat:jiiybi3ffveihbtvvww7glr55y

Prediction of Core Shear Strength in Sandwich Composites using Deep Learning and Support Vector Regression

2019 International journal of recent technology and engineering  
The results obtained revealed that the deep learning model develo ped provides better predictive ability than the model of SVR.  ...  In the current approach deep learning and SVR models were worked out by taking on six different parameters namely foam density, aging temperature and variety of fiber types as input variables.  ...  The main reason for profound learning is the idea that artificial intelligence should inspire the brain [14] .].In several conditions, deep learning algorithms tally the brain as each the brain and deep  ... 
doi:10.35940/ijrte.a1971.078219 fatcat:px42j3yf5ngcjajqg73e5fyvby

Biomechanics of Concussion

David F. Meaney, Douglas H. Smith
2011 Clinics in Sports Medicine  
INTRODUCTION AND SCOPE The recent public awareness of mild traumatic brain injury (TBI) (concussion) and the possible long-term consequences on brain function has raised the profile of the disorder and  ...  On a relative basis, the incidence of mild TBI far exceeds the number of TBI-related fatalities and moderate/severe TBIs, with some estimates suggesting its frequency is at least 10 times more common than  ...  As computational power and modeling algorithms becomes more advanced, these models will eventually become available for rapid use on desktop computers.  ... 
doi:10.1016/j.csm.2010.08.009 pmid:21074079 pmcid:PMC3979340 fatcat:mewdlsfxlzevlh5vyyk66zboua

White matter changes in microstructure associated with a maladaptive response to stress in rats

R Magalhães, J Bourgin, F Boumezbeur, P Marques, M Bottlaender, C Poupon, B Djemaï, E Duchesnay, S Mériaux, N Sousa, T M Jay, A Cachia
2017 Translational Psychiatry  
The results possibly reveal an adaptation of the SD strain to the stressful stimuli through synaptic and structural plasticity processes, possibly reflecting learning processes.  ...  These effects were localized on the left side of the brain on the external capsule, corpus callosum, deep cerebral white matter, anterior commissure, endopiriform nucleus, dorsal hippocampus and amygdala  ...  This work was performed on a platform of France Life Imaging (FLI) network partly funded by the grant ANR-11-INBS-0006.  ... 
doi:10.1038/tp.2016.283 pmid:28117841 pmcid:PMC5545740 fatcat:mp3xs3a5mjeyzkag3p6n5rce4e

A Novel Approach for Predicting Atrial Fibrillation Recurrence After Ablation Using Deep Convolutional Neural Networks by Assessing Left Atrial Curved M-Mode Speckle-Tracking Images

Yi-Ting Hwang, Hui-Ling Lee, Cheng-Hui Lu, Po-Cheng Chang, Hung-Ta Wo, Hao-Tien Liu, Ming-Shien Wen, Fen-Chiung Lin, Chung-Chuan Chou
2021 Frontiers in Cardiovascular Medicine  
novel approach using deep CNNs for learning features of left atrial curved M-mode speckle-tracking images seems to be optimal for classifying outcome status after AF ablation.  ...  Aims: Curved M-mode images of global strain (GS) and strain rate (GSR) provide sufficiently detailed spatiotemporal information of deformation mechanics.  ...  Deep learning, a class of machine-learning algorithms using multiple layers to progressively extract higher level features from raw input, has become a powerful method of classifying several diseases  ... 
doi:10.3389/fcvm.2020.605642 pmid:33553257 pmcid:PMC7862331 fatcat:nbip5u67mne6bag2atbc3lqyee

A machine-vision approach for automated pain measurement at millisecond timescales [article]

Jessica Jones, William Foster, Colin Twomey, Justin Burdge, Osama Ahmed, Jessica A Wojick, Gregory Corder, Joshua B Plotkin, Ishmail Abdus-Saboor
2020 bioRxiv   pre-print
Here we capture rapid paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches.  ...  Our statistical software platform, PAWS (Pain Assessment at Withdrawal Speeds), uses a univariate projection of paw position over time to automatically quantify fast paw dynamics at the onset of paw withdrawal  ...  Here we capture rapid paw kinematics during pain behavior in mice with high-speed videography and automated paw tracking with machine and deep learning approaches.  ... 
doi:10.1101/2020.02.18.955070 fatcat:clbk4falevbxjo672jevpewepq

Machine learning based multiscale calibration of mesoscopic constitutive models for composite materials: application to brain white matter

Duncan Field, Yanis Ammouche, José-Maria Peña, Antoine Jérusalem
2021 Computational Mechanics  
AbstractA modular pipeline for improving the constitutive modelling of composite materials is proposed.The method is leveraged here for the development of subject-specific spatially-varying brain white  ...  Finally, a machine learning layer is implemented to predict the constitutive model parameters directly from any new microstructure.  ...  Daniel Garcia-Gonzalez for the discussions on the constitutive model.  ... 
doi:10.1007/s00466-021-02009-1 fatcat:2fnvhe4itbfqlilrsqkugkd6xe

Weakly Supervised Attention Model for RV StrainClassification from volumetric CTPA Scans [article]

Noa Cahan, Edith M. Marom, Shelly Soffer, Yiftach Barash, Eli Konen, Eyal Klang, Hayit Greenspan
2021 arXiv   pre-print
This could be used as a second reader, alerting for high-risk PE patients. To the best of our knowledge, there are no previous deep learning-based studies that attempted to solve this problem.  ...  We developed a weakly supervised deep learning algorithm, with an emphasis on a novel attention mechanism, to automatically classify RV strain on CTPA.  ...  To the best of our knowledge, there is no prior deep learning-based solution for fully automated classification of RV strain using CTPA, and this is the first work where medical images are used in such  ... 
arXiv:2107.12009v1 fatcat:wivctqpowjgxtepootsbxddgqe

Incorporating the image formation process into deep learning improves network performance in deconvolution applications [article]

Yue Li, Yijun Su, Min Guo, Xiaofei Han, Jiamin Liu, Harshad Vishwasrao, Xuesong Li, Ryan Christensen, Titas Sengupta, Mark Moyle, Jiji Chen, Ted B. Usdin (+4 others)
2022 bioRxiv   pre-print
We present 'Richardson-Lucy Network' (RLN), a fast and lightweight deep learning method for 3D fluorescence microscopy deconvolution.  ...  RLN outperforms Richardson-Lucy deconvolution on volumes contaminated with severe out of focus fluorescence or noise and provides 4- to 6-fold faster reconstructions of large, cleared tissue datasets than  ...  We thank Zhirong Bao for providing the OD58 C. elegans strain used in Fig. 1b and Supplementary Figs. 10-11, Johnny Bui, Grant Kroeschell, and Matthew Chaw for maintaining the worm strains, and W.  ... 
doi:10.1101/2022.03.05.483139 fatcat:pbfxs73yuvh6ljqq4wvtzeyp5q

Holographic deep learning for rapid optical screening of anthrax spores

YoungJu Jo, Sangjin Park, JaeHwang Jung, Jonghee Yoon, Hosung Joo, Min-hyeok Kim, Suk-Jo Kang, Myung Chul Choi, Sang Yup Lee, YongKeun Park
2017 Science Advances  
We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning.  ...  The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features.  ...  The method proposed in this paper solves this difficulty by using the powerful learning abilities of deep neural networks.  ... 
doi:10.1126/sciadv.1700606 pmid:28798957 pmcid:PMC5544395 fatcat:dorpklbbnjgvjpadgwmog5xxme

Age-dependency of the serum oxidative level in the senescence-accelerated mouse prone 8

Sakiko TANIGUCHI, Masakazu HANAFUSA, Hirokazu TSUBONE, Haruka TAKIMOTO, Daisuke YAMANAKA, Masayoshi KUWAHARA, Koichi ITO
2016 Journal of Veterinary Medical Science  
SAMP8 showed earlier increase of d-ROM value with age than SAM resistant 1 (SAMR1), the control strain.  ...  In this study, to estimate the oxidative stress level in senescence-accelerated mouse prone 8 (SAMP8), we evaluated serum reactive oxygen species production and reduction capacity by measurement of Diacron-Reactive  ...  The senescence-accelerated mouse prone 8 (SAMP8) is one of the strains which shows a short life-span or rapid advancement of senescence, having been used as a representative for aging studies due to exhibiting  ... 
doi:10.1292/jvms.16-0204 pmid:27149963 pmcid:PMC5053944 fatcat:nd2xlhxvfvhelobo7fjzocbuk4

Pseudotargeted Metabolomic Fingerprinting and Deep Learning for Identification and Visualization of Common Pathogens

Ying Feng, Moutong Chen, Xianhu Wei, Honghui Zhu, Jumei Zhang, Youxiong Zhang, Liang Xue, Lanyan Huang, Guoyang Chen, Minling Chen, Yu Ding, Qingping Wu
2022 Frontiers in Microbiology  
We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen.  ...  However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures.  ...  We used the VAE-CNN model that combined pseudotargeted metabolomics technology and deep learning technology to realize the identification of foodborne pathogens and the visualization of classification.  ... 
doi:10.3389/fmicb.2022.830832 pmid:35359729 pmcid:PMC8960985 fatcat:lzia2hbvkbgqdfaxt2it3cxtua
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