Keyword search (3,448 papers available)


Fast oscillations >40 Hz localize the epileptogenic zone: An electrical source imaging study using high-density electroencephalography.

Author(s): Avigdor T, Abdallah C, von Ellenrieder N, Hedrich T, Rubino A, Lo Russo G, Bernhardt B, Nobili L, Grova C, Frauscher B...

OBJECTIVE: Fast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source...

Article GUID: 33450578
Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes.

Author(s): Lessard-Tremblay M, Weeks J, Morelli L, Cowan G, Gagnon G, Zednik RJ

Traditional capacitive electrocardiogram (cECG) electrodes suffer from limited patient comfort, difficulty of disinfection and low signal-to-noise ratio in addition to the challenge of integrating them in wearables. A novel hybrid flexible cECG electrode wa...

Article GUID: 32927651
The antibacterial activity of p-tert-butylcalix[6]arene and its effect on a membrane model: molecular dynamics and Langmuir film studies.

Author(s): Wrobel EC, de Lara LS, do Carmo TAS, Castellen P, Lazzarotto M, de Lázaro SR, Camilo A, Caseli L, Schmidt R, DeWolf CE, Wohnrath K

Phys Chem Chem Phys. 2020 Mar 03;: Authors: Wrobel EC, de Lara LS, do Carmo TAS, Castellen P, Lazzarotto M, de Lázaro SR, Camilo A, Caseli L, Schmidt R, DeWolf CE, Wohnrath K

Article GUID: 32124897
Computer-Aided Diagnosis System of Alzheimer's Disease Based on Multimodal Fusion: Tissue Quantification Based on the Hybrid Fuzzy-Genetic-Possibilistic Model and Discriminative Classification Based on the SVDD Model.

Author(s): Lazli L, Boukadoum M, Ait Mohamed O

Brain Sci. 2019 Oct 22;9(10): Authors: Lazli L, Boukadoum M, Ait Mohamed O

Article GUID: 31652635
The first MICCAI challenge on PET tumor segmentation.

Author(s): Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky...

Med Image Anal. 2018 02;44:177-195 Authors: Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P...

Article GUID: 29268169
Caloric restriction extends yeast chronological lifespan via a mechanism linking cellular aging to cell cycle regulation, maintenance of a quiescent state, entry into a non-quiescent state and survival in the non-quiescent state.

Author(s): Leonov A, Feldman R, Piano A, Arlia-Ciommo A, Lutchman V, Ahmadi M, Elsaser S, Fakim H, Heshmati-Moghaddam M, Hussain A, Orfali S, Rajen H, ...

Oncotarget. 2017 Sep 19;8(41):69328-69350 Authors: Leonov A, Feldman R, Piano A, Arlia-Ciommo A, Lutchman V, Ahmadi M, Elsaser S, Fakim H, Heshmati-Moghaddam M, Hussain A, Orfali S, Rajen H, Roofi...

Article GUID: 29050207
Expedition Cognition: A Review and Prospective of Subterranean Neuroscience With Spaceflight Applications.

Author(s): Mogilever NB, Zuccarelli L, Burles F, Iaria G, Strapazzon G, Bessone L, Coffey EBJ

Front Hum Neurosci. 2018;12:407 Authors: Mogilever NB, Zuccarelli L, Burles F, Iaria G, Strapazzon G, Bessone L, Coffey EBJ

Article GUID: 30425628
Human Physiology During Exposure to the Cave Environment: A Systematic Review With Implications for Aerospace Medicine.

Author(s): Zuccarelli L, Galasso L, Turner R, Coffey EJB, Bessone L, Strapazzon G

Front Physiol. 2019;10:442 Authors: Zuccarelli L, Galasso L, Turner R, Coffey EJB, Bessone L, Strapazzon G

Article GUID: 31068833
Title:The first MICCAI challenge on PET tumor segmentation.
Authors:Hatt M,  Laurent B,  Ouahabi A,  Fayad H,  Tan S,  Li L,  Lu W,  Jaouen V,  Tauber C,  Czakon J,  Drapejkowski F,  Dyrka W,  Camarasu-Pop S,  Cervenansky F,  Girard P,  Glatard T,  Kain M,  Yao Y,  Barillot C,  Kirov A,  Visvikis D
Link:https://www.ncbi.nlm.nih.gov/pubmed/29268169?dopt=Abstract
DOI:10.1016/j.media.2017.12.007
Category:Med Image Anal
PMID:29268169
Dept Affiliation: IMAGING
1 LaTIM, UMR 1101, INSERM, IBSAM, UBO, UBL, Brest, France. Electronic address: hatt@univ-brest.fr.
2 LaTIM, UMR 1101, INSERM, IBSAM, UBO, UBL, Brest, France.
3 Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
4 Memorial Sloan-Kettering Cancer Center, New-York, USA.
5 INSERM, UMR 930, Imaging and brain, University of Tours, France.
6 Stermedia Sp. z o. o., ul. A. Ostrowskiego 13, Wroclaw, Poland.
7 Stermedia Sp. z o. o., ul. A. Ostrowskiego 13, Wroclaw, Poland; Wroclaw University of Science and Technology, Faculty of Fundamental Problems of Technology, Department of Biomedical Engineering, Poland.
8 Université de Lyon, CREATIS, CNRS UMR5220, INSERM UMR 1044, INSA-Lyon, Université Lyon 1, Lyon, France.
9 Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada.
10 INRIA, Visages project-team, CNRS, IRISA 6074, INSERM, Visages, UMR 1228, University of Rennes I, Rennes Cx 35042, France.

Description:

The first MICCAI challenge on PET tumor segmentation.

Med Image Anal. 2018 02;44:177-195

Authors: Hatt M, Laurent B, Ouahabi A, Fayad H, Tan S, Li L, Lu W, Jaouen V, Tauber C, Czakon J, Drapejkowski F, Dyrka W, Camarasu-Pop S, Cervenansky F, Girard P, Glatard T, Kain M, Yao Y, Barillot C, Kirov A, Visvikis D

Abstract

INTRODUCTION: Automatic functional volume segmentation in PET images is a challenge that has been addressed using a large array of methods. A major limitation for the field has been the lack of a benchmark dataset that would allow direct comparison of the results in the various publications. In the present work, we describe a comparison of recent methods on a large dataset following recommendations by the American Association of Physicists in Medicine (AAPM) task group (TG) 211, which was carried out within a MICCAI (Medical Image Computing and Computer Assisted Intervention) challenge.

MATERIALS AND METHODS: Organization and funding was provided by France Life Imaging (FLI). A dataset of 176 images combining simulated, phantom and clinical images was assembled. A website allowed the participants to register and download training data (n?=?19). Challengers then submitted encapsulated pipelines on an online platform that autonomously ran the algorithms on the testing data (n?=?157) and evaluated the results. The methods were ranked according to the arithmetic mean of sensitivity and positive predictive value.

RESULTS: Sixteen teams registered but only four provided manuscripts and pipeline(s) for a total of 10 methods. In addition, results using two thresholds and the Fuzzy Locally Adaptive Bayesian (FLAB) were generated. All competing methods except one performed with median accuracy above 0.8. The method with the highest score was the convolutional neural network-based segmentation, which significantly outperformed 9 out of 12 of the other methods, but not the improved K-Means, Gaussian Model Mixture and Fuzzy C-Means methods.

CONCLUSION: The most rigorous comparative study of PET segmentation algorithms to date was carried out using a dataset that is the largest used in such studies so far. The hierarchy amongst the methods in terms of accuracy did not depend strongly on the subset of datasets or the metrics (or combination of metrics). All the methods submitted by the challengers except one demonstrated good performance with median accuracy scores above 0.8.

PMID: 29268169 [PubMed - indexed for MEDLINE]