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A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation

Hongzhi Wang, Sandhitsu R. Das, Jung Wook Suh, Murat Altinay, John Pluta, Caryne Craige, Brian Avants, Paul A. Yushkevich
2011 NeuroImage  
The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper  ...  The method then attempts to correct such errors in segmentations produced by the host method on new images.  ...  Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org/).  ... 
doi:10.1016/j.neuroimage.2011.01.006 pmid:21237273 pmcid:PMC3049832 fatcat:bmigv3hu25dlpazhxgdezst2bu

A comparison of accurate automatic hippocampal segmentation methods

Azar Zandifar, Vladimir Fonov, Pierrick Coupé, Jens Pruessner, D. Louis Collins
2017 NeuroImage  
We also apply error correction to the four automatic segmentation methods, and complete a comprehensive validation to investigate differences between the methods.  ...  Our study shows that the nonlinear patch-based segmentation method with error correction is the most accurate automatic segmentation method and yields the most conformity with manual segmentation (κ =  ...  Acknowledgments This work was supported by grants from the Canadian Institutes of Health Research (MOP-111169), les Fonds de Research Santé Québec Pfizer Innovation fund (25262), and an NSERC CREATE Grant  ... 
doi:10.1016/j.neuroimage.2017.04.018 pmid:28404458 fatcat:bzai2vksefe7ncqqnj4b7dmioy

A Semi-Automated Pipeline for the Segmentation of Rhesus Macaque Hippocampus: Validation across a Wide Age Range

Michael R. Hunsaker, David G. Amaral, Noam Harel
2014 PLoS ONE  
Any systematic errors in the normalization process are corrected using a machine-learning algorithm that has been trained by comparing manual and automated segmentations to identify systematic errors.  ...  This report outlines a neuroimaging pipeline that allows a robust, high-throughput, semi-automated, template-based protocol for segmenting the hippocampus in rhesus macaque (Macaca mulatta) monkeys ranging  ...  Acknowledgments The authors would like to thank Joshua K. Lee and Naomi J. Goodrich-Hunsaker, Ph.D. for helpful conversations concerning the methods used in the present experiment.  ... 
doi:10.1371/journal.pone.0089456 pmid:24586791 pmcid:PMC3933562 fatcat:by5u3y7cenh2hlj7s7q6xs6pqa

BEaST: Brain extraction based on nonlocal segmentation technique

Simon F. Eskildsen, Pierrick Coupé, Vladimir Fonov, José V. Manjón, Kelvin K. Leung, Nicolas Guizard, Shafik N. Wassef, Lasse Riis Østergaard, D. Louis Collins
2012 NeuroImage  
To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction.  ...  This method is based on nonlocal segmentation embedded in a multi-resolution framework.  ...  Acknowledgements The authors would like to thank Professor Nick Fox, Dementia Research Centre, Institute of Neurology, London, for contributing with the ADNI semi-automatic brain segmentations. This  ... 
doi:10.1016/j.neuroimage.2011.09.012 pmid:21945694 fatcat:qbd3t6xtbjhvzimkbobr3teqxy

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  
The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge.  ...  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  ...  As an alternative or complement to either approach, Wang et al. (2011) proposed to use machine learning techniques to learn systematic segmentation errors that are then corrected in a post-processing  ... 
doi:10.1016/j.media.2014.12.003 pmid:25596765 fatcat:vqiz457hcfa7viaoi3ffcwzd2i

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  
However, there are several protocols to define the hippocampus anatomically in vivo, and the method used to generate atlases may impact automatic accuracy and sensitivity - particularly in pathologically  ...  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  ...  We would like to thank Gregory Szilagyi for coding a multiplexing batch script in Python and the anonymous reviewers for their helpful comments.  ... 
doi:10.1016/j.neuroimage.2012.10.081 pmid:23142652 pmcid:PMC3606906 fatcat:l4je6rxxjjdvdj2gqjampsjnii

Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation [article]

Petteri Teikari, Marc Santos, Charissa Poon, Kullervo Hynynen
2016 arXiv   pre-print
Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images.  ...  We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result.  ...  Acknowledgements We would like to thank Sharan Sankar for his work as a summer student writing wrapper for various wrappers for ITK C++ functions.  ... 
arXiv:1606.02382v1 fatcat:v5jwomv4gbf7bhxe6yn2tbkhiq

Efficient Morphometric Techniques in Alzheimer's Disease Detection: Survey and Tools

Vinutha N., P. Deepa Shenoy, P. Deepa Shenoy, K.R. Venugopal
2016 Neuroscience International  
The development of advance techniques in the multiple fields such as image processing, data mining and machine learning are required for the early detection of Alzheimer's Disease (AD) and to prevent the  ...  The different types of segmentation techniques such as Tissue Segmentation, Atlas based Segmentation, Hippocampus Segmentation and other segmentation techniques have been discussed.  ...  Author's Contributions All authors were involved in the manuscript preparation. Ethics This article is original and contains unpublished material.  ... 
doi:10.3844/amjnsp.2016.19.44 fatcat:3zeb2s5pjzfv7mptqi7cy2a3au

Correction: Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

Fariborz Mahmoudi, Kost Elisevich, Hassan Bagher-Ebadian, Mohammad-Reza Nazem-Zadeh, Esmaeil Davoodi-Bojd, Jason M. Schwalb, Manpreet Kaur, Hamid Soltanian-Zadeh
2018 PLoS ONE  
[This corrects the article DOI: 10.1371/journal.pone.0199137.].  ...  Special thanks to Saeed Shokri, Harrini Vijay, and Mario Dervishi for their invaluable help in data processing and Rushna Ali for editing the manuscript.  ...  Acknowledgments This work was supported in part by NIH grant R01EB013227.  ... 
doi:10.1371/journal.pone.0209866 pmid:30571727 pmcid:PMC6301553 fatcat:woi3y4ib6reh5l5wzgftpvkka4

Data mining MR image features of select structures for lateralization of mesial temporal lobe epilepsy

Fariborz Mahmoudi, Kost Elisevich, Hassan Bagher-Ebadian, Mohammad-Reza Nazem-Zadeh, Esmaeil Davoodi-Bojd, Jason M. Schwalb, Manpreet Kaur, Hamid Soltanian-Zadeh, Boris C Bernhardt
2018 PLoS ONE  
Results Using a logistic regression classifier, the volumes of both hippocampus and amygdala showed correct lateralization rates of 94.1%.  ...  The MR image data set consisted of 54 patients with imaging evidence for hippocampal sclerosis (HS-P) and 14 patients without (HS-N).  ...  Special thanks to Saeed Shokri, Harrini Vijay, and Mario Dervishi for their invaluable help in data processing and Rushna Ali for editing the manuscript.  ... 
doi:10.1371/journal.pone.0199137 pmid:30067753 pmcid:PMC6070173 fatcat:gvmkgx3dafbv7b5hmlvt6hkunq

Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review

Ahmad Chaddad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, Tamim Niazi
2021 Diagnostics  
This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects.  ...  Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks.  ...  In [88] , a hybrid model consisting of CNN with brain features was used to improve the performance metrics.  ... 
doi:10.3390/diagnostics11112032 pmid:34829379 pmcid:PMC8618159 fatcat:elkk2rupifgetbwj3lshb2dhqi

NEUROIMAGING AND PATTERN RECOGNITION TECHNIQUES FOR AUTOMATIC DETECTION OF ALZHEIMER'S DISEASE: A REVIEW

Rupali Kamathe, Kalyani Joshi
2017 ICTACT Journal on Image and Video Processing  
A combination of brain imaging and clinical tests for checking the signs of memory impairment is used to identify patients with AD.  ...  This paper is about the review of such semi or fully automatic techniques with detail comparison of methods implemented, class labels considered, data base used and the results obtained for related study  ...  ACKNOWLEDGMENT Authors are thankful to Dr. Rohit Sangolkar, Nizam's institute of Medical Sciences, Hyderabad and Dr.  ... 
doi:10.21917/ijivp.2017.0219 fatcat:33i5bd3v7fbxhapbs35a2lipby

Predicting Alzheimer's disease by classifying 3D-Brain MRI images using SVM and other well-defined classifiers

S Matoug, A Abdel-Dayem, K Passi, W Gross, M Alqarni
2012 Journal of Physics, Conference Series  
We discuss an automatic scheme that reads volumetric MRI, extracts the middle slices of the brain region, performs 2-dimensional (volume slices) and volumetric segmentation methods in order to segment  ...  Advanced medical imaging and pattern recognition techniques are good tools to create a learning database in the first step and to predict the class label of incoming data in order to assess the development  ...  An example of a wrapper method for nonlinear SVMs can be found in [39] , where instead of minimising the classification error, the features are selected to minimise a generalisation error bound.  ... 
doi:10.1088/1742-6596/341/1/012019 fatcat:niplou5avndppjjswtyeka2qfq

Factorisation-based Image Labelling [article]

Yu Yan, Yael Balbastre, Mikael Brudfors, John Ashburner
2021 arXiv   pre-print
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.  ...  Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable.  ...  A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation.  ... 
arXiv:2111.10326v2 fatcat:jubdlnpbdrhz3ktdngb7o5nz7i

The Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

Michael W. Weiner, Dallas P. Veitch, Paul S. Aisen, Laurel A. Beckett, Nigel J. Cairns, Robert C. Green, Danielle Harvey, Clifford R. Jack, William Jagust, Enchi Liu, John C. Morris, Ronald C. Petersen (+8 others)
2013 Alzheimer's & Dementia  
Wang et al [76] presented a wrapper algorithm that can be used in conjunction with automatic segmentation methods to correct such consistent bias.  ...  The algorithm uses machine learning methods to first learn the pattern of consistent segmentation errors and then applies a bias correction to the mislabeled voxels detected in the initial step.  ... 
doi:10.1016/j.jalz.2013.05.1769 pmid:23932184 pmcid:PMC4108198 fatcat:epydl5poq5evrobxws3qerdolu
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