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Robust multi-site MR data processing: iterative optimization of bias correction, tissue classification, and registration

Eun Young Kim, Hans J. Johnson
2013 Frontiers in Neuroinformatics  
The benefits of these enhancements were investigated by a series of experiments with both simulated brain data set (BrainWeb) and by applying to highly-heterogeneous data from a 32 site imaging study with  ...  In this paper, we describe enhancements to a joint registration, bias correction, and the tissue classification, that improve the generalizability and robustness for processing multi-modal longitudinal  ...  ACKNOWLEDGMENTS We wish to express our gratitude to University of Iowa Scalable Informatics, Neuroimaging, Analysis, Processing, and Software Engineering (SINAPSE) laboratory team members for their all  ... 
doi:10.3389/fninf.2013.00029 pmid:24302911 pmcid:PMC3831347 fatcat:hv7a6kc5jvhtxbsv2dduhv4u3y

Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests

Anthony Bianchi, James V. Miller, Ek Tsoon Tan, Albert Montillo
2013 2013 IEEE 10th International Symposium on Biomedical Imaging  
Accurate automated segmentation of brain tumors in MR images is challenging due to overlapping tissue intensity distributions and amorphous tumor shape.  ...  Third, we demonstrate further accuracy enhancement by extending our long range features from 100mm to a full 200mm.  ...  Texture Features Texture is a compact representation of a local neighborhood. In brain MRI, texture gives a description of the underlying tissue in a region.  ... 
doi:10.1109/isbi.2013.6556583 pmid:25404996 pmcid:PMC4232942 fatcat:27c2v2pxgffezpokfkt27hlbny

Prediction of subthalamic nucleus neural tissue dimensions using high compactness microrecording recording system in Parkinson's with deep brain stimulation

Venkateshwarla Rama Raju
2021 IP Indian Journal of Neurosciences  
PD Subject image Computational simulation of clinically effective tissue activation and patient MRI. technical computing Mat Lab tool.  ...  Tissue movement dimensions simulated/or-modeled from clinically-identified stimulus parameters of dissimilar implants period areas over, in, and/or beneath the sub thalamic nuclei.  ...  Prediction of subthalamic nucleus neural tissue dimensions using high compactness microrecording recording system in Parkinson's with deep brain stimulation. IP Indian J Neurosci 2021;7(1):67-75.  ... 
doi:10.18231/j.ijn.2021.010 fatcat:dm6jwthbqnarbdvjjnwt2awv54

Optimizing Neuro-Oncology Imaging: A Review of Deep Learning Approaches for Glioma Imaging

Madeleine M. Shaver, Paul A. Kohanteb, Catherine Chiou, Michelle D. Bardis, Chanon Chantaduly, Daniela Bota, Christopher G. Filippi, Brent Weinberg, Jack Grinband, Daniel S. Chow, Peter D. Chang
2019 Cancers  
Radiographic assessment with magnetic resonance imaging (MRI) is widely used to characterize gliomas, which represent 80% of all primary malignant brain tumors.  ...  Deep learning, a subset of machine learning artificial intelligence, has gained traction as a method, which has seen effective employment in solving image-based problems, including those in medical imaging  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/cancers11060829 pmid:31207930 pmcid:PMC6627902 fatcat:gc37sfqmr5ce7i535ig6jyszc4

Degenerative Adversarial NeuroImage Nets for Brain Scan Simulations: Application in Ageing and Dementia [article]

Daniele Ravi, Stefano B. Blumberg, Silvia Ingala, Frederik Barkhof, Daniel C. Alexander, Neil P. Oxtoby
2021 arXiv   pre-print
To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer's Disease Neuroimaging Initiative dataset and held out a separate test set of 1283  ...  In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration  ...  Acknowledgements The authors would like to thank NVIDIA Corporation for the donation of one of the GPUs used for this research.  ... 
arXiv:1912.01526v5 fatcat:kwltawwk6ra7lfwypzavz4qsvi

Noise contamination from PET blood sampling pump: Effects on structural MRI image quality in simultaneous PET/MR studies

Elizabeth Bartlett, Christine DeLorenzo, Ramin Parsey, Chuan Huang
2017 Medical Physics (Lancaster)  
While many cortical regions showed a percent difference of less than 1% with the pump, regions close to tissue-air interfaces, subject to larger susceptibility artifacts, were significantly affected.  ...  Purpose-To fully quantify PET imaging outcome measures, a blood sampling pump is often used during the PET acquisition.  ...  Acknowledgments We would like to acknowledge support provided by the NARSAD Young Investigator Award (PI: Huang).  ... 
doi:10.1002/mp.12715 pmid:29210075 pmcid:PMC6022403 fatcat:3r676r3bafbkzlnsmwtm52taqm

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring

Shanaka Ramesh Gunasekara, H N T K Kaldera, Maheshi B Dissanayake
2021 Journal of Healthcare Engineering  
First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor  ...  As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is  ...  Acknowledgments e authors acknowledge the insightful and extremely helpful reviews and comments provide by Dr.  ... 
doi:10.1155/2021/6695108 pmid:33777346 pmcid:PMC7948532 fatcat:fvdgaswtj5gtvkhirxyi44cbye

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

Bassem A. Abdullah
2012 Open Biomedical Engineering Journal  
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data.  ...  The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with  ...  The training set entries were fed to the SVM engine to generate a MS classifier which is able to classify any square wxw block of a brain MRI slice as MS block (y=1) or non-MS block (y=0) based on its  ... 
doi:10.2174/1874230001206010056 pmid:22741026 pmcid:PMC3382289 fatcat:rnn4zffor5gaza2fc77hsz4lqa

Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

Bassem A. Abdullah
2012 Open Biomedical Engineering Journal  
In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data.  ...  The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with  ...  The training set entries were fed to the SVM engine to generate a MS classifier which is able to classify any square wxw block of a brain MRI slice as MS block (y=1) or non-MS block (y=0) based on its  ... 
doi:10.2174/1874120701206010056 fatcat:qzr77ooombe53ohyx6kor7nn4u

Multivariate Analyses Applied to Healthy Neurodevelopment in Fetal, Neonatal, and Pediatric MRI

Jacob Levman, Emi Takahashi
2016 Frontiers in Neuroanatomy  
This paper presents the results of a systematic review of the literature focusing on MVA applied to healthy subjects in fetal, neonatal and pediatric magnetic resonance imaging (MRI) of the brain.  ...  MVA can be used to address a wide variety of neurological medical imaging related challenges including the evaluation of healthy brain development, the automated analysis of brain tissues and structures  ...  ACKNOWLEDGMENTS This article was supported by the National Institute of Health grants R01HD078561 and R03NS091587 to ET.  ... 
doi:10.3389/fnana.2015.00163 pmid:26834576 pmcid:PMC4720794 fatcat:wctsqbvl6baq5m7dm4snvry5lu

Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree

Wen-Hung Chao, You-Yin Chen, Chien-Wen Cho, Sheng-Huang Lin, Yen-Yu I. Shih, Siny Tsang
2008 Journal of Neuroscience Methods  
The purpose of this study was to improve the accuracy rate of brain tissue classification in magnetic resonance (MR) imaging using a boosted decision tree segmentation algorithm.  ...  Herein, we examined simulated phantom MR (SPMR) images, simulated brain MR (SBMR) images, and a real data.  ...  Acknowledgments This study was supported by grant NSC 95-2221-E-009-171-MY3 from the National Science Council of the Republic of China and grant VGHUST96-P5-19 from VGHUST Joint Research Program, Tsou's  ... 
doi:10.1016/j.jneumeth.2008.08.017 pmid:18786567 fatcat:gxbasezhojcz3fgjuwnfwuzvbu

Automatic skull segmentation from MR images for realistic volume conductor models of the head: Assessment of the state-of-the-art

Jesper D. Nielsen, Kristoffer H. Madsen, Oula Puonti, Hartwig R. Siebner, Christian Bauer, Camilla Gøbel Madsen, Guilherme B. Saturnino, Axel Thielscher
2018 NeuroImage  
However, automatic skull reconstruction from structural magnetic resonance (MR) images is difficult, as compact bone has a very low signal in magnetic resonance imaging (MRI).  ...  In particular, the skull compartment exerts a strong influence on the field distribution due to its low conductivity, suggesting the need to represent its geometry accurately.  ...  NNF14OC0011413) and a PhD stipend of the Sino-Danish Center to JDN.  ... 
doi:10.1016/j.neuroimage.2018.03.001 pmid:29518567 fatcat:riw2j3a55ffpfjmlbu5efqryj4

A method for localizing microelectrode trajectories in the macaque brain using MRI

Rishi M. Kalwani, Luke Bloy, Mark A. Elliott, Joshua I. Gold
2009 Journal of Neuroscience Methods  
After surgical implantation, recording chambers are fitted with a plastic cylinder that is filled with a high-contrast agent to aid in the segmentation of the cylinder from brain matter in an MRI volume  ...  Magnetic resonance imaging (MRI) is often used by electrophysiologists to target specific brain regions for placement of microelectrodes.  ...  A series of steps was necessary to view our structural MRI data in relation to a specific brain atlas in Caret (Van Essen et al., 2001) .  ... 
doi:10.1016/j.jneumeth.2008.08.034 pmid:18831988 pmcid:PMC2632859 fatcat:5rrvc2cpr5fmpiaa4m2kp7abkq

AiCNNs (Artificially-integrated Convolutional Neural Networks) for Brain Tumor Prediction

Ansh Mittal, Deepika Kumar
2019 EAI Endorsed Transactions on Pervasive Health and Technology  
OBJECTIVES: This paper propose a model named Artificially-integrated Convolutional Neural Networks (AiCNNs) that accurately classifies brain MRI scans to 3 classes of brain tumor and negative diagnosis  ...  INTRODUCTION: Accurate analysis of brain MRI images is vital for diagnosing brain tumor in its nascent stages. Automated classification of brain tumor is an important step for accurate diagnosis.  ...  This research essentially made it possible for MRI simulators to efficiently create 3D brain images.  ... 
doi:10.4108/eai.12-2-2019.161976 fatcat:fseyyc3zgzhi7cfxv7wimzmfj4

Whole-brain atrophy assessed by proportional- versus registration-based pipelines from 3T MRI in multiple sclerosis

Christopher C. Hemond, Renxin Chu, Subhash Tummala, Shahamat Tauhid, Brian C. Healy, Rohit Bakshi
2018 Brain and Behavior  
Conclusion: Whole-brain atrophy metrics may not be interchangeable between proportional-and registration-based automated pipelines from 3T MRI in patients with MS.  ...  Whole-brain atrophy was assessed by two automated pipelines: (a) SPM8 to derive brain parenchymal fraction (BPF, proportional-based method); (b) SIENAX to derive normalized brain parenchymal volume (BPV  ...  bias field correction enabled (orange highlight), registration to the MNI-152 template to determine the skullbased scaling factor, and intensity normalization and tissue class segmentation using a Markov  ... 
doi:10.1002/brb3.1068 pmid:30019857 pmcid:PMC6085901 fatcat:laiosovnpvh7deqsoojz6dgoim
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