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Performance of five research-domain automated WM lesion segmentation methods in a multi-center MS study

Alexandra de Sitter, Martijn D. Steenwijk, Aurélie Ruet, Adriaan Versteeg, Yaou Liu, Ronald A. van Schijndel, Petra J.W. Pouwels, Iris D. Kilsdonk, Keith S. Cover, Bob W. van Dijk, Stefan Ropele, Maria A. Rocca (+13 others)
2017 NeuroImage  
In this work, five research-domain automated segmentation methods were evaluated using a multi-center MS dataset.  ...  Automated lesion segmentation methods have been developed to substitute manual outlining, but evidence of their performance in multi-center investigations is lacking.  ...  The aim of this study was, firstly, to evaluate the performance of research-domain automated WM lesion segmentation methods in a multi-center MS dataset with diverging scanners and protocols.  ... 
doi:10.1016/j.neuroimage.2017.09.011 pmid:28899746 fatcat:r2i5ibhvnnc5jha7b54ydnvvzq

Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review)

Eleftherios Kontopodis, Efrosini Papadaki, Eleftherios Trivzakis, Thomas Maris, Panagiotis Simos, Georgios Papadakis, Aristidis Tsatsakis, Demetrios Spandidos, Apostolos Karantanas, Kostas Marias
2021 Experimental and Therapeutic Medicine  
The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.  ...  Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification  ...  method is suitable for larger, multi-center studies.  ... 
doi:10.3892/etm.2021.10583 pmid:34504594 pmcid:PMC8393268 fatcat:yolthcmsgfhdbbwwpcn5nte2ly

Manual and automated tissue segmentation confirm the impact of thalamus atrophy on cognition in Multiple Sclerosis: a multicenter study

Jessica Burggraaff, Yao Liu, Juan C. Prieto, Jorge Simoes, Alexandra de Sitter, Serena Ruggieri, Iman Brouwer, Birgit I. Lissenberg-Witte, Mara A. Rocca, Paola Valsasina, Stefan Ropele, Claudio Gasperini (+13 others)
2020 NeuroImage: Clinical  
Thalamus segmentations were generated manually and using five automated methods.  ...  Thalamus atrophy has been linked to cognitive decline in multiple sclerosis (MS) using various segmentation methods.  ...  Automated segmentation methods Five automated segmentation programs were used to measure the volume of the thalamus.  ... 
doi:10.1016/j.nicl.2020.102549 pmid:33401136 pmcid:PMC7787946 fatcat:h4e7hcxknbgqdeh4ifwjkbp75e

Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications [article]

Yang Ma, Chaoyi Zhang, Mariano Cabezas, Yang Song, Zihao Tang, Dongnan Liu, Weidong Cai, Michael Barnett, Chenyu Wang
2022 arXiv   pre-print
Further, we review technical strategies, such as domain adaptation, to enhance MS lesion segmentation in real-world clinical settings.  ...  Here, we provide a comprehensive review of state-of-the-art automatic statistical and deep-learning MS segmentation methods and discuss current and future clinical applications.  ...  Multi-center Studies Private MRI databases are commonly used for designing state-of-the-art methods, some of which are not released to the research community.  ... 
arXiv:2104.10029v3 fatcat:elds3foafrdc5ireld5wahp5ra

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks [article]

Sergi Valverde, Mostafa Salem, Mariano Cabezas, Deborah Pareto, Joan C. Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Joaquim Salvi, Arnau Oliver, Xavier Lladó
2018 arXiv   pre-print
In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method.  ...  In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior  ...  The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN-X PASCAL GPU used in this research.  ... 
arXiv:1805.12415v1 fatcat:dbjwvg7suna4nbf2srxqdn5xoe

Fuzzy Multi-channel Clustering with Individualized Spatial Priors for Segmenting Brain Lesions and Infarcts [chapter]

Evangelia I. Zacharaki, Guray Erus, Anastasios Bezerianos, Christos Davatzikos
2012 IFIP Advances in Information and Communication Technology  
Assessment on a population of 47 patients from different imaging sites illustrates the potential of the proposed method in segmenting both hyperintense lesions and necrotic infarcts.  ...  In this paper, we present a semi-supervised segmentation methodology that detects and classifies cerebrovascular disease in multi-channel magnetic resonance (MR) images.  ...  This research was supported by a Marie Curie International Reintegration Grant within the 7 th European Community Framework Programme.  ... 
doi:10.1007/978-3-642-33412-2_8 fatcat:mj26attc5bhotjs6v2xfakjxly

One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks

Sergi Valverde, Mostafa Salem, Mariano Cabezas, Deborah Pareto, Joan C. Vilanova, Lluís Ramió-Torrentà, Àlex Rovira, Joaquim Salvi, Arnau Oliver, Xavier Lladó
2018 NeuroImage: Clinical  
In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method.  ...  In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior  ...  The authors gratefully acknowledge the support of the NVIDIA Corporation with their donation of the TITAN-X PASCAL GPU used in this research.  ... 
doi:10.1016/j.nicl.2018.101638 pmid:30555005 pmcid:PMC6413299 fatcat:oa2pc2mpunazlezksgjrhombsa

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson
2017 Journal of digital imaging  
First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions.  ...  Next, the performance, speed, and properties of deep learning approaches are summarized and discussed.  ...  Acknowledgements This work was supported by National Institutes of  ... 
doi:10.1007/s10278-017-9983-4 pmid:28577131 pmcid:PMC5537095 fatcat:lekbdtmkx5cchmuutntacymrzu

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Aaron Carass, Snehashis Roy, Amod Jog, Jennifer L. Cuzzocreo, Elizabeth Magrath, Adrian Gherman, Julia Button, James Nguyen, Ferran Prados, Carole H. Sudre, Manuel Jorge Cardoso, Niamh Cawley (+34 others)
2017 NeuroImage  
We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms  ...  The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points.  ...  There has been less work on automated methods for serial lesion segmentation (segmentation of lesions for the same subject over different time-points).  ... 
doi:10.1016/j.neuroimage.2016.12.064 pmid:28087490 pmcid:PMC5344762 fatcat:vdau3k5hpffbxmrbc6jcnti6gm

Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images

Mehdi SadeghiBakhi, Hamidreza Pourreza, Hamidreza Mahyar
2022 IEEE Journal of Translational Engineering in Health and Medicine  
The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately.  ...  A multitude of multimodality automatic biomedical approaches are used to segment lesions that are not beneficial for patients in terms of cost, time, and usability.  ...  These challenges motivate Deep Learning (DL) and Machine Learning (ML) researchers to propose and develop a fast and accurate approach for the segmentation of MS lesions in MRI [5] .  ... 
doi:10.1109/jtehm.2022.3172025 pmid:35711337 pmcid:PMC9191687 fatcat:eke3flvnvnge3lw6qq3563vjke

Multi-branch convolutional neural network for multiple sclerosis lesion segmentation

Shahab Aslani, Michael Dayan, Loredana Storelli, Massimo Filippi, Vittorio Murino, Maria A. Rocca, Diego Sona
2019 NeuroImage  
In this paper, we present an automated approach for segmenting multiple sclerosis (MS) lesions from multi-modal brain magnetic resonance images.  ...  On the private dataset, using the same array of performance metrics as in the ISBI challenge, the proposed approach shows high improvements in MS lesion segmentation compared with other publicly available  ...  Among automated methods, supervised ML algorithms can learn from previously labeled training data and provide high performance in MS lesion segmentation.  ... 
doi:10.1016/j.neuroimage.2019.03.068 pmid:30953833 fatcat:f3gl3sgqbfevxp4jit6muvr5yq

Abstracts from the ASENT 2010 annual meeting March 4–6, 2010

Jeffrey T. Apter, Robert J. Fox, Thomas Cronin, Jian Lin, Ken Sakaie, Daniel Ontaneda, Shamseldeen Y. Mahmoud, Mark J. Lowe, Michael D. Phillips, Xiaofeng Wang, Daniel Goldberg-Zimring, Christian D. Chavarro-Nieto (+72 others)
2010 Neurotherapeutics  
In the future, it is likely that symptomatic treatments will be utilized with AD modifying therapies, the development of which are currently a primary focus of research.  ...  Four clinical studies in Ͼ125 healthy normal human subjects have been completed with EVP-6124, including a single-ascending-dose study, a 14-day multiple-ascending-dose study, a 21-day multiple-dose study  ...  Previous studies have shown that DTI can be successfully implemented in a multi-center study with good comparability between centers.  ... 
doi:10.1016/j.nurt.2010.06.003 fatcat:gf6pnuyg3fa5ncqnr32cv7sjte

Spinal cord grey matter segmentation challenge

Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso, Benjamin N. Conrad, Esha Datta, Gergely Dávid, Benjamin De Leener, Sara M. Dupont, Patrick Freund (+16 others)
2017 NeuroImage  
Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D  ...  There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation.  ...  The neural network used in that study was optimized for lesion segmentation in the brain in subjects with multiple sclerosis (MS).  ... 
doi:10.1016/j.neuroimage.2017.03.010 pmid:28286318 pmcid:PMC5440179 fatcat:lj3qt3zrxvefxjdt6itd2vlpby

A Contrast-Adaptive Methodfor Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

Stefano Cerri, Oula Puonti, Dominik S. Meier, Jens Wuerfel, Mark Mühlau, Hartwig R. Siebner, Koen Van Leemput
2020 NeuroImage  
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple sclerosis patients.  ...  We validate the method using four disparate datasets, showing robust performance in white matter lesion segmentation while simultaneously segmenting dozens of other brain structures.  ...  on a Siemens Trio 3T scanner at the Danish Research Center of Magnetic Resonance (DRCMR).  ... 
doi:10.1016/j.neuroimage.2020.117471 pmid:33099007 pmcid:PMC7856304 fatcat:4omt2kz2hzglblvzowhmkxnm6i

The association between intra- and juxta-cortical pathology and cognitive impairment in multiple sclerosis by quantitative T 2 * mapping at 7 T MRI

Céline Louapre, Sindhuja T. Govindarajan, Costanza Giannì, Nancy Madigan, A. Scott Nielsen, Jacob A. Sloane, Revere P. Kinkel, Caterina Mainero
2016 NeuroImage: Clinical  
These findings may guide studies at lower field strength designed to develop surrogate markers of cognitive impairment in MS.  ...  Thirty-one MS patients underwent neuropsychological evaluation, acquisition of 7 T multi-echo T 2 * gradientecho sequences, and 3 T anatomical images for cortical surfaces reconstruction.  ...  WM lesion volume WM lesions were segmented on magnitude images from 7 T singleecho FLASH T 2 * scans with a semi-automated tool in 3D Slicer version 4.2.0 (http://www.slicer.org) (CL, CG, CM, 7, 3 and  ... 
doi:10.1016/j.nicl.2016.11.001 pmid:27872810 pmcid:PMC5107649 fatcat:7htvhpzqkjc33ov3j4zdh2tu44
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