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Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images

Lichi Zhang, Qian Wang, Yaozong Gao, Hongxin Li, Guorong Wu, Dinggang Shen
2017 Neurocomputing  
In this paper, we propose a concatenated set of spatially-localized random forests for multi-atlas-based hippocampus labeling of adult/infant brain MR images.  ...  Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies.  ...  Conclusion In this paper, we present a novel concatenated learning framework for hippocampus labeling in both adult and infant MR brain images.  ... 
doi:10.1016/j.neucom.2016.05.082 pmid:28133417 pmcid:PMC5268165 fatcat:v4urcn3d65g5rnd5ituwwxesdu

Hippocampal Segmentation from Longitudinal Infant Brain MR Images via Classification-guided Boundary Regression Y

Yeqin Shao, Jaeil Kim, Yaozong Gao, Qian Wang, Weili Lin, Dinggang Shen
2019 IEEE Access  
However, most of the hippocampal segmentation methods were developed for population-based adult brain images, which are not suitable for longitudinal infant brain images acquired in the first year of life  ...  Hippocampal segmentation from infant brain MR images is indispensable for studying early brain development.  ...  the deformable model for final hippocampal segmentation in the infant brain MR images.  ... 
doi:10.1109/access.2019.2904143 fatcat:d57rup3ejfeflkachkgya324te

Dilated Dense U-Net for Infant Hippocampus Subfield Segmentation

Hancan Zhu, Feng Shi, Li Wang, Sheng-Che Hung, Meng-Hsiang Chen, Shuai Wang, Weili Lin, Dinggang Shen
2019 Frontiers in Neuroinformatics  
Accurate and automatic segmentation of infant hippocampal subfields from magnetic resonance (MR) images is an important step for studying memory related infant neurological diseases.  ...  In this paper, we propose a new fully convolutional network (FCN) for infant hippocampal subfield segmentation by embedding the dilated dense network in the U-net, namely DUnet.  ...  ACKNOWLEDGMENTS This work utilizes data collected by a NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.  ... 
doi:10.3389/fninf.2019.00030 pmid:31068797 pmcid:PMC6491864 fatcat:e7cnqfplbjhj3itzbkcwii7fvy

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods

Ahmed Serag, Manuel Blesa, Emma J. Moore, Rozalia Pataky, Sarah A. Sparrow, A. G. Wilkinson, Gillian Macnaught, Scott I. Semple, James P. Boardman
2016 Scientific Reports  
We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases).  ...  The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has  ...  Babak Ardekani, and Mr. Jimit Doshi for providing the software, answering our questions and the suggestions of how to tune parameters.  ... 
doi:10.1038/srep23470 pmid:27010238 pmcid:PMC4806304 fatcat:mzjusbtyarbtvjjmuavnnst2gy

3D-MASNet: 3D Mixed-scale Asymmetric Convolutional Segmentation Network for 6-month-old Infant Brain MR Images [article]

Zilong Zeng, Tengda Zhao, Lianglong Sun, Yihe Zhang, Mingrui Xia, Xuhong Liao, Jiaying Zhang, Dinggang Shen, Li Wang, Yong He
2021 bioRxiv   pre-print
Precise segmentation of infant brain MR images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is essential for studying neuroanatomical hallmarks of early brain development.  ...  Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infant.  ...  The MR images of 10 infants with manual labels were provided for model 6 training and validation. The images of 13 infants without labels were provided for model testing.  ... 
doi:10.1101/2021.05.23.445294 fatcat:f5yjszfr3banngr7tlhkzu6ygy

Deep Learning in Medical Image Analysis

Dinggang Shen, Guorong Wu, Heung-Il Suk
2017 Annual Review of Biomedical Engineering  
The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field.  ...  On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images.  ...  the image data and reusable to various medical imaging applications, such as hippocampus segmention (88) and prostate localization in MR images (85, 86) .  ... 
doi:10.1146/annurev-bioeng-071516-044442 pmid:28301734 pmcid:PMC5479722 fatcat:amn6qgpt6fedzp3zejgi4aw66u

Conventional and Deep Learning Methods for Skull Stripping in Brain MRI

Hafiz Zia Ur Rehman, Hyunho Hwang, Sungon Lee
2020 Applied Sciences  
Skull stripping in brain magnetic resonance volume has recently been attracting attention due to an increased demand to develop an efficient, accurate, and general algorithm for diverse datasets of the  ...  Accurate skull stripping is a critical step for neuroimaging diagnostic systems because neither the inclusion of non-brain tissues nor removal of brain parts can be corrected in subsequent steps, which  ...  It incorporates a discriminative (random forest) and a generative model (point distribution) with the graph cuts for whole-brain extraction.  ... 
doi:10.3390/app10051773 fatcat:nwp2z2y4jzgoxinv7sfppbfrfa

The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results

Yaser ElNakieb, Mohamed T. Ali, Ahmed Elnakib, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Gregory Neal Barnes, Ayman El-Baz
2021 Sensors  
This paper provides a thorough study of implementing feature engineering tools to find discriminant insights from brain imaging of white matter connectivity and using a machine learning framework for an  ...  Previous literature suggests differences in brain development, environmental, and/or genetic factors play a role in developing autism, yet scientists still do not know exactly the pathology of this disorder  ...  In [17], a sample of 38 infants from the Infant Brain Imaging Study (IBIS) were used for the diagnosis of autism using spherical harmonics.  ... 
doi:10.3390/s21248171 pmid:34960265 pmcid:PMC8703859 fatcat:uin46u43nbgclnlc2smkb4fqde

Classifying Alzheimer's disease, Lewy body dementia, and normal controls using 3D texture analysis in magnetic resonance images

Ketil Oppedal, Kjersti Engan, Trygve Eftestøl, Mona Beyer, Dag Aarsland
2017 Biomedical Signal Processing and Control  
It seems like the results do not differ much when performing analysis in different regions of the brain and that the results vary in an inconsistent way.  ...  and used in classification with the aim to serve as a tool for computer aided diagnosis (CAD) in dementia.  ...  In the previous study we concluded that 2D texture analysis calculated from WM and WML regions in 3DT1 MR images of the brain used in a random forest classifier, is able to classify subjects with dementia  ... 
doi:10.1016/j.bspc.2016.10.007 fatcat:5wzpvtujt5c7feyjki24s2npsi

Poster Session I

2013 Neuropsychopharmacology  
Methods: Subjects were given 5 mg/day open label folate for 3 months.  ...  Considering evidence for impairments in both nicotinic and glutamatergic transmission in SZ, these data in animals provide support for the continued focus on the a7nAChR and NMDA receptor as targets for  ...  Abi-Dargham, and S. Kapur, Arch Gen Psychiatry 69, 776 (2012). 2  ... 
doi:10.1038/npp.2013.279 fatcat:54ipecxjarcvljrvn5fgtgif5u

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.  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  They classified the nodes in the generated MCI-graph using GCN and Cheby-GCN and compared the results with a Ridge, a random forest classifier and a multilayer perceptron, and demonstrated a high performance  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Poster Session III

2010 Neuropsychopharmacology  
Brains and blood serum were collected and processed for analysis.  ...  complex), a key brain region for several cognitive and neuropsychiatric-disorders such as obsessive-compulsive disorder (OCD), Tourette Syndrome (TS), and addictive disorders.  ...  Cholesterol and flotillin, markers for lipid rafts, peaked in factions 4-5 whereas the transferrin receptor and total protein, markers for non-raft membranes, peaked in fractions 9-11.  ... 
doi:10.1038/npp.2010.218 fatcat:i5knteqsgveybffza7g6b4iqei

Poster Session I

2015 Neuropsychopharmacology  
An initial one-sample, voxel-wise t-test to describe the spatial distribution of GBC, which was followed by a predictor analysis, where we evaluated those voxels in the brain where CTQ score significantly  ...  The present study was to explore neural effects of yoga vs memory training in older adults with subjective memory complaints.  ...  Eleven healthy controls, matched for sex, age, and binding status, were also imaged with [11C] PBR28 PET.  ... 
doi:10.1038/npp.2015.325 pmid:26632286 pmcid:PMC4672310 fatcat:cqqyl6zydfeuxltv6zkqhn7qry

Poster Session I

2014 Neuropsychopharmacology  
Conclusions: Importantly for human studies, many of the IFN-induced transcripts in the brain are also influenced in the peripheral blood.  ...  Methods: Cortical EEG was recorded in adult male C57BL/6 mice (n ¼ 6) from the primary motor (frontal, F) and primary visual (occipital, O) areas, using epidural stainless steel screw electrodes implanted  ...  Diagnostic high resolution anatomical brain MRI was performed on all subjects to exclude the existence of any pathology and to be used for MRS voxel localizations.  ... 
doi:10.1038/npp.2014.280 fatcat:xunw4pdn75hlnmhvvksgp2lsse

About Sleep's Role in Memory

Björn Rasch, Jan Born
2013 Physiological Reviews  
For each data point, each participant completed 6 -8 trials, with the different retention intervals performed in random order.  ...  Specifically, newer findings characterize sleep as a brain state optimizing memory consolidation, in opposition to the waking brain being optimized for encoding of memories.  ...  in preparing the manuscript and Sandra Ackermann-Wohlgemuth, Luciana Besedovsky, Susanne Diekelmann, and Ines Wilhelm for helpful comments  ... 
doi:10.1152/physrev.00032.2012 pmid:23589831 pmcid:PMC3768102 fatcat:hwyquoixznaofmhcrp2ipgixie
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