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A direct morphometric comparison of five labeling protocols for multi-atlas driven automatic segmentation of the hippocampus in Alzheimer's disease

Sean M. Nestor, Erin Gibson, Fu-Qiang Gao, Alex Kiss, Sandra E. Black
2013 NeuroImage  
For the first time we test head-to-head the performance of five common hippocampal labeling protocols for multi-atlas based segmentation, using both the Sunnybrook Longitudinal Dementia Study and the entire  ...  Together, these results suggest that selection of a candidate protocol for fully automatic multi-template based segmentation in AD can influence both segmentation accuracy when compared to expert manual  ...  Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904  ... 
doi:10.1016/j.neuroimage.2012.10.081 pmid:23142652 pmcid:PMC3606906 fatcat:l4je6rxxjjdvdj2gqjampsjnii

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI

Thomas Shaw, Ashley York, Maryam Ziaei, Markus Barth, Steffen Bollmann
2020 NeuroImage  
The volumetric and morphometric examination of hippocampus formation subfields in a longitudinal manner using in vivo MRI could lead to more sensitive biomarkers for neuropsychiatric disorders and diseases  ...  We examined the performance of a new longitudinal pipeline (Longitudinal Automatic Segmentation of Hippocampus Subfields [LASHiS]) against three freely available, published approaches.  ...  Hippocampus subfield segmentation results (coloured) for a single 1028 representative subject for the five tested methods at the same slice in a coronal 1029 view.  ... 
doi:10.1016/j.neuroimage.2020.116798 pmid:32311467 fatcat:eidytseltbfvljkweaa4erwhra

Longitudinal Automatic Segmentation of Hippocampal Subfields (LASHiS) using Multi-Contrast MRI [article]

Thomas B Shaw, Steffen Bollmann, Ashley York, Maryam Ziaei, Markus Barth
2019 bioRxiv   pre-print
The volumetric and morphometric examination of hippocampus formation subfields in a longitudinal manner using in vivo MRI could lead to more sensitive biomarkers for neuropsychiatric disorders and diseases  ...  We examined the performance of a new longitudinal pipeline (Longitudinal Automatic Segmentation of Hippocampus Subfields [LASHiS]) against three freely available, published approaches.  ...  Acknowledgements The authors acknowledge the facilities and scientific and technical assistance of the National Imaging Facility, a National Collaborative Research Infrastructure Strategy  ... 
doi:10.1101/759217 fatcat:34pid5v56rgmrjgbfmf5bfslmi

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy [article]

Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab, Christian Wachinger
2018 arXiv   pre-print
in comparison to state-of-the-art methods, while being orders of magnitude faster.  ...  Whole brain segmentation from structural magnetic resonance imaging (MRI) is a prerequisite for most morphological analyses, but is computationally intense and can therefore delay the availability of image  ...  Acknowledgment Support for this research was provided in part by the Bavarian State Ministry of Education, Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B).  ... 
arXiv:1801.04161v2 fatcat:qwb525dpabesbixaptvieutk4y

Robust whole-brain segmentation: Application to traumatic brain injury

Christian Ledig, Rolf A. Heckemann, Alexander Hammers, Juan Carlos Lopez, Virginia F.J. Newcombe, Antonios Makropoulos, Jyrki Lötjönen, David K. Menon, Daniel Rueckert
2015 Medical Image Analysis  
We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called "Multi-Atlas Label Propagation with Expectation-Maximisation based refinement" (MALP-EM  ...  In this context we show that MALP-EM is competitive for the segmentation of MR brain scans of healthy adults when compared to state-of-the-art automatic labelling techniques.  ...  This allows a variable weight for the contribution of either registration-driven (multi-atlas label propagation) or intensity-driven (EM-refinement) label estimates in the final segmentation.  ... 
doi:10.1016/j.media.2014.12.003 pmid:25596765 fatcat:vqiz457hcfa7viaoi3ffcwzd2i

Multi-atlas based representations for Alzheimer's disease diagnosis

Rui Min, Guorong Wu, Jian Cheng, Qian Wang, Dinggang Shen
2014 Human Brain Mapping  
In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases.  ...  In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a predefined atlas) via non-linear registration, thus  ...  INTRODUCTION Morphometric pattern analysis is one of the most popular approaches for automatic Alzheimer's disease (AD) diagnosis.  ... 
doi:10.1002/hbm.22531 pmid:24753060 pmcid:PMC4169318 fatcat:v7aytj2bszeejbsqrtevnnszzq

A high-resolution computational atlas of the human hippocampus from postmortem magnetic resonance imaging at 9.4 T

P YUSHKEVICH, B AVANTS, J PLUTA, S DAS, D MINKOFF, D MECHANICHAMILTON, S GLYNN, S PICKUP, W LIU, J GEE
2009 NeuroImage  
The atlas is provided as an online resource with the aim of supporting subfield segmentation in emerging hippocampus imaging and image analysis techniques.  ...  This paper describes the construction of a computational anatomical atlas of the human hippocampus. The atlas is derived from high-resolution 9.4 Tesla MRI of postmortem samples.  ...  Postmortem MR studies described in this manuscript were performed in the Small Animal Imaging Facility in the Department of Radiology at the University of Pennsylvania.  ... 
doi:10.1016/j.neuroimage.2008.08.042 pmid:18840532 pmcid:PMC2650508 fatcat:ee5iccsorjhtpijjlqfsew4lle

A review on brain structures segmentation in magnetic resonance imaging

Sandra González-Villà, Arnau Oliver, Sergi Valverde, Liping Wang, Reyer Zwiggelaar, Xavier Lladó
2016 Artificial Intelligence in Medicine  
A c c e p t e d M a n u s c r i p t Identifying the brain structures is a key aspect for cognitive disease diagnosis. We present a review of automatic brain structures segmentation methods.  ...  Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches.  ...  González-Villà holds a UdG-BRGR2015 grant from the University of Girona.  ... 
doi:10.1016/j.artmed.2016.09.001 pmid:27926381 fatcat:morzi7uy6zch7p73l7t7mrq2ti

Differential medial temporal lobe and default-mode network functional connectivity and morphometric changes in Alzheimer's disease

Kamil A. Grajski, Steven L. Bressler
2019 NeuroImage: Clinical  
in Alzheimer's Disease), hippocampus, parahippocampal gyrus and temporal pole, and cortical regions comprising or co-activated with the default-mode network, including rostral and medial prefrontal cortex  ...  These findings position medial temporal lobe resting state functional connectivity as a candidate biomarker of an Alzheimer's Disease pathophysiological cascade, potentially in advance of clinical biomarkers  ...  Acknowledgements Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department  ... 
doi:10.1016/j.nicl.2019.101860 pmid:31158694 pmcid:PMC6545401 fatcat:bskvlfu52nhqtbebg2k3scuffq

Measurement of hippocampal volume changes in serial MRI scans

Julia A. Schnabel, Louis Lemieux, U. C. Wieshmann, Simon R. Arridge, Kenneth M. Hanson
1999 Medical Imaging 1999: Image Processing  
We present a new method for the detection and measurement of volume changes in human hippocampi in serial Magnetic Resonance Imaging (MRI).  ...  The method follows a two-stage approach: (1) precise co-registration and intensity matching of the initial (baseline) and follow-up scan, and (2) refinement and segmentation propagation of the hippocampi  ...  Schnabel was supported by the Leverhulme Trust, and by the Netherlands Science Organization (NWO).  ... 
doi:10.1117/12.348535 dblp:conf/miip/SchnabelLWA99 fatcat:4fdebg7pd5elxhpmi652crpazy

A comparison of automated segmentation and manual tracing in estimating hippocampal volume in ischemic stroke and healthy control participants

Mohamed Salah Khlif, Natalia Egorova, Emilio Werden, Alberto Redolfi, Marina Boccardi, Charles S. DeCarli, Evan Fletcher, Baljeet Singh, Qi Li, Laura Bird, Amy Brodtmann
2019 NeuroImage: Clinical  
The automation of hippocampal segmentation has been investigated in normal aging, epilepsy, and in Alzheimer's disease.  ...  These findings recommend an automated segmentation without lesion masking as a more reliable procedure for the estimation of hippocampal volume in ischemic stroke.  ...  The authors would also like to thank the Victorian Life Sciences Computation Initiative in the University of Melbourne (http://www.vlsci.org.au/) for support of data supercomputing in SGI Altix XE Cluster  ... 
doi:10.1016/j.nicl.2018.10.019 pmid:30606656 pmcid:PMC6411582 fatcat:mc2lmg2x2fehblmrdxdvgfrhym

An evaluation of four automatic methods of segmenting the subcortical structures in the brain

Kolawole Oluwole Babalola, Brian Patenaude, Paul Aljabar, Julia Schnabel, David Kennedy, William Crum, Stephen Smith, Tim Cootes, Mark Jenkinson, Daniel Rueckert
2009 NeuroImage  
Each method was applied to the segmentation of 18 subcortical structures in 270 subjects from a diverse pool varying in age, disease, sex and image acquisition parameters.  ...  Two methods are atlas-basedclassifier fusion and labelling (CFL) and expectation-maximisation segmentation using a brain atlas (EMS), and two incorporate statistical models of shape and appearanceprofile  ...  We are grateful to Christian Haselgrove and the Center for Morphometric Analysis, Boston, for the MR images used and to the different groups that provided the images.  ... 
doi:10.1016/j.neuroimage.2009.05.029 pmid:19463960 fatcat:mh45ybbocfcqlocwhdfmjbp6ua

Deformable Templates Guided Discriminative Models for Robust 3D Brain MRI Segmentation

Cheng-Yi Liu, Juan Eugenio Iglesias, Zhuowen Tu
2013 Neuroinformatics  
It takes advantage of the adaptation capability of the Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.edu).  ...  One major challenge is to design and learn effective image models accounting for the large variability in anatomy and data acquisition protocols.  ...  Data collection and sharing for this project was also funded by the ADNI (National Institutes of Health grant U01 AG024904).  ... 
doi:10.1007/s12021-013-9190-5 pmid:23836390 pmcid:PMC5966025 fatcat:yvb3w7afybfavbbvu52axx6a5q

Differential medial temporal lobe and default-mode network functional connectivity and morphometric changes in Alzheimers disease [article]

Kamil A Grajski, Steven L Bressler
2017 bioRxiv   pre-print
A novel data-driven approach for ROI identification and selection was developed and implemented with the AFNI toolkit.  ...  These findings position resting-state functional MRI connectivity biomarkers within a model Alzheimers disease pathophysiological cascade as coincident with and potentially in advance of morphometric biomarkers  ...  analysis of five task-free resting state networks 610 (RSN), including DMN, in a cohort drawn from the Knight Alzheimer's Disease Research 611 All rights reserved.  ... 
doi:10.1101/232165 fatcat:n4nphj5vjjgedlhr4pvnhjftl4

High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

Ramon Casanova, Christopher T. Whitlow, Benjamin Wagner, Jeff Williamson, Sally A. Shumaker, Joseph A. Maldjian, Mark A. Espeland
2011 Frontiers in Neuroinformatics  
The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients.  ...  In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status.  ...  We also study how informative different brain tissues and morphometric measures are for automatic classification of sMRI in AD.  ... 
doi:10.3389/fninf.2011.00022 pmid:22016732 pmcid:PMC3193072 fatcat:uoexyqzkfzbd5aebjsn2yifgqe
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