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Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images

Pim Moeskops, Max A. Viergever, Manon J. N. L. Benders, Ivana Išgum, Sébastien Ourselin, Martin A. Styner
2015 Medical Imaging 2015: Image Processing  
The aim of this work is to evaluate a method developed for neonatal images in the segmentation of adult images.  ...  The aim of the currently presented study is to evaluate whether this method, developed for the segmentation of neonatal MR images, can be employed for the segmentation of adult patients.  ...  DISCUSSION This paper presented evaluation of an automatic brain segmentation method developed for preterm infants on images of adult patients.  ... 
doi:10.1117/12.2081833 dblp:conf/miip/MoeskopsVBI15 fatcat:pvti5e647fb7dbq24d55fmjrwi

From neonatal to adult brain MR image segmentation in a few seconds using 3D-like fully convolutional network and transfer learning

Yongchao Xu, Thierry Geraud, Isabelle Bloch
2017 2017 IEEE International Conference on Image Processing (ICIP)  
To the best of our knowledge, this is the first method that applies transfer learning to segment both neonatal and adult brain 3D MR images.  ...  Brain magnetic resonance imaging (MRI) is widely used to assess brain development in neonates and to diagnose a wide range of neurological diseases in adults.  ...  This makes particularly difficult the development of a universal method for both neonates and adults.  ... 
doi:10.1109/icip.2017.8297117 dblp:conf/icip/XuGB17 fatcat:xc4q7hkcbbgnhiwxtiqwsoafum

A review on automatic fetal and neonatal brain MRI segmentation

Antonios Makropoulos, Serena J. Counsell, Daniel Rueckert
2018 NeuroImage  
In recent years, a variety of segmentation methods have been proposed for automatic delineation of the fetal and neonatal brain MRI.  ...  Challenges relating to the image acquisition, the rapid brain development as well as the limited availability of imaging data however hinder this segmentation task.  ...  The fetal/neonatal MR images exhibit an inverted WM/GM contrast compared to the adult data.  ... 
doi:10.1016/j.neuroimage.2017.06.074 pmid:28666878 fatcat:7fbaimevcfc67eqpdi2zx7l5ey

Automatic segmentation of the intracranialvolume in fetal MR images [article]

N. Khalili, P. Moeskops, N.H.P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M.J.N.L. Benders, M.A. Viergever, J.P.W. Pluim, I. Išgum
2017 arXiv   pre-print
This paper presents an automatic method for segmentation of the ICV in fetal MR images.  ...  Quantitative analysis of fetal brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV).  ...  Acknowledgements This study was sponsored by the Research Program Specialized Nutrition of the Utrecht Center for Food and Health, through a subsidy from the Dutch Ministry of Economic Affairs, the Utrecht  ... 
arXiv:1708.02282v1 fatcat:ltqt5u7fg5drrfaec567q3yxse

Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks

N. Khalili, E. Turk, M.J.N.L. Benders, P. Moeskops, N.H.P. Claessens, R. de Heus, A. Franx, N. Wagenaar, J.M.P.J. Breur, M.A. Viergever, I. Išgum
2019 NeuroImage: Clinical  
We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans.  ...  Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans.  ...  Acknowledgments This study was sponsored by the Research Program Specialized Nutrition of the Utrecht Center for Food and Health, through a subsidy from the Dutch Ministry of Economic Affairs, the Utrecht  ... 
doi:10.1016/j.nicl.2019.102061 pmid:31835284 pmcid:PMC6909142 fatcat:kpdiz5b4mbgfzekpvq7xn2pwbu

Automatic segmentation of the hippocampus for preterm neonates from early-in-life to term-equivalent age

Ting Guo, Julie L. Winterburn, Jon Pipitone, Emma G. Duerden, Min Tae M. Park, Vann Chau, Kenneth J. Poskitt, Ruth E. Grunau, Anne Synnes, Steven P. Miller, M. Mallar Chakravarty
2015 NeuroImage: Clinical  
The present study focuses on the development and validation of an automatic segmentation protocol that is based on the MAGeT-Brain (Multiple Automatically Generated Templates) algorithm to delineate the  ...  Methods: First, we present a three-step manual segmentation protocol to delineate the hippocampus for preterm neonates and apply this protocol on 22 early-in-life and 22 term images.  ...  neonatal MR images gradually alter and invert to become similar to those on children and adult images.  ... 
doi:10.1016/j.nicl.2015.07.019 pmid:26740912 pmcid:PMC4561668 fatcat:upmc2zatgnbodb3sn3gaz64nqu

Automatic Segmentation of Eight Tissue Classes in Neonatal Brain MRI

Petronella Anbeek, Ivana Išgum, Britt J. M. van Kooij, Christian P. Mol, Karina J. Kersbergen, Floris Groenendaal, Max A. Viergever, Linda S. de Vries, Manon J. N. L. Benders, Bogdan Draganski
2013 PLoS ONE  
We propose an automatic method for probabilistic brain segmentation in neonatal MRIs.  ...  Materials and Methods: In an IRB-approved study axial T1-and T2-weighted MR images were acquired at termequivalent age for a preterm cohort of 108 neonates.  ...  Automatic methods developed for segmentation of adult brain with MRI are generally not applicable for segmentation in neonatal scans.  ... 
doi:10.1371/journal.pone.0081895 pmid:24358132 pmcid:PMC3866108 fatcat:7msnmnqqyfd47ngasrj7jrab5q

Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. de Vries, Manon J. N. L. Benders, Ivana Isgum
2016 IEEE Transactions on Medical Imaging  
This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network.  ...  Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.  ...  The authors thank Bennett Landman for providing the data of the MICCAI challenge on multi-atlas labelling.  ... 
doi:10.1109/tmi.2016.2548501 pmid:27046893 fatcat:7kcpxd3jgzb7hixo6swanw2tsa

Brain Age Estimation Using Multiple Regression Analysis in Brain MR Images

Saadia Binte Alam, Ryosuke Nakano, Syoji Kobashi
2016 International Journal of Innovative Computing, Information and Control  
Physiological age estimation based on human brain MR images has been an interesting research field over the past years.  ...  To evaluate brain deformation, this paper proposes an estimation method for both neonatal and adult brain age using manifold learning, principal component analysis, followed by multiple regression models  ...  This method automatically detects landmarks of adult subjects from MR images using a set of analysis tools from FSL, developed by Analysis group, FMRIB, Oxford, UK to analyze brain imaging like functional  ... 
doi:10.24507/ijicic.12.04.1385 fatcat:2takzmkvvzhwhh4fiv35h2gpjm

Infant Brain Atlases from Neonates to 1- and 2-Year-Olds

Feng Shi, Pew-Thian Yap, Guorong Wu, Hongjun Jia, John H. Gilmore, Weili Lin, Dinggang Shen, Hitoshi Okazawa
2011 PLoS ONE  
State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation  ...  The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size.  ...  As reported in [17] , MR imaging indicates that the neonatal brain is only half the volume of adult brain, and grows to about 90% adult brain volume at the end of the second year.  ... 
doi:10.1371/journal.pone.0018746 pmid:21533194 pmcid:PMC3077403 fatcat:xuw5l6g3xfhfvafsez2cmzxamm

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

Challenges in pediatric neuroimaging

Matthew J. Barkovich, Yi Li, Rahul S. Desikan, A. James Barkovich, Duan Xu
2018 NeuroImage  
Pediatric neuroimaging is challenging due the rapid structural, metabolic, and functional changes that occur in the developing brain.  ...  New pre-and post-processing techniques can also compensate for the motion artifacts and low signal that often degrade neonatal scans.  ...  Barkovich was supported by the National Institutes of Health (NIBIB) T32 Training Grant, T32EB001631 RSD was supported by the Radiological Society of North America, ASNR Foundation AD Imaging Award, National  ... 
doi:10.1016/j.neuroimage.2018.04.044 pmid:29684645 pmcid:PMC6197938 fatcat:5tojca2wsjdw3irlsilknd3vuq

SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests

Ahmed Serag, Alastair G. Wilkinson, Emma J. Telford, Rozalia Pataky, Sarah A. Sparrow, Devasuda Anblagan, Gillian Macnaught, Scott I. Semple, James P. Boardman
2017 Frontiers in Neuroinformatics  
As partial volume effects in neonatal brain MRI present challenges for automatic 396 segmentation methods, we evaluated the influence of each of the features on 397 segmentation accuracy of the neonatal  ...  ). 79 80 To address these challenges, here we describe a method for automatic brain 81 segmentation of MR images, called SEGMA (SEGMentation Approach).  ... 
doi:10.3389/fninf.2017.00002 pmid:28163680 pmcid:PMC5247463 fatcat:gguunf3lyrcrbi4ozmi56ssrzm

Probabilistic Brain Tissue Segmentation in Neonatal Magnetic Resonance Imaging

Petronella Anbeek, Koen L Vincken, Floris Groenendaal, Annemieke Koeman, Matthias J P van Osch, Jeroen Van der Grond
2008 Pediatric Research  
A fully automated method has been developed for segmentation of four different structures in the neonatal brain: white matter (WM), central gray matter (CEGM), cortical gray matter (COGM), and cerebrospinal  ...  The described method is based on routine diagnostic magnetic resonance imaging (MRI) and is suitable for large population studies.  ...  Methods for adult brain segmentation by a multichannel approach (13-16) may be suitable for neonates as well.  ... 
doi:10.1203/pdr.0b013e31815ed071 pmid:18091357 fatcat:6pqf6qno35hjpp4eqyu4rqrxjy

Estimation 0f the Total Brain Volume Using Semi-Automatic Segmentation and Stereology of the Newborns' Brain MRI

Tolga Ertekin, Niyazi Acer, Semra Icer, Afra Yıldırım
2013 NeuroQuantology  
From these results, it can be concluded that the semi-automated segmentation method and stereological technique can be used for reliable volume estimation of total brains in neonates.  ...  Based on these techniques we compared here in, the clinician may evaluate the growth of the brain in a more efficient and precise manner.  ...  Dr. for skilful technical assistance.  ... 
doi:10.14704/nq.2013.11.2.631 fatcat:3xwg6kdmsjdd7obsj4qvekjlqq
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