Maximino Bessa - IEEE Xplore Author Profile

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Author details

Maximino Bessa

Publications

65

Citations

569

Publications by Year

20102024

Co-Authors:

Telmo AdãoVasco AmorimMiguel BacelarLuís BarbosaJoão Barreira

Show All Co-Authors (70)

Maximino Bessa

Affiliation

University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

Institute for Systems and Computer Engineering

Technology and Science

Porto, Portugal

Publication Topics

Virtually,
Immersive Virtual Reality,
Head-mounted Display,
Virtual Reality Experience,
Spatial Presence,
Virtual Reality System,
Virtual Reality Technology,
User Experience,
Virtual Reality Applications,
Immersive Experience,
Online Training,
Sociodemographic Questionnaire

Biography

Maximino Bessa has been a Senior Researcher with the INESC TEC, since 2009. He is currently an Associate Professor, with Habilitation, of the Department of Engineering, University of Trás-os-Montes and Alto Douro, Portugal. He is also the Director of the Multisensory Virtual Reality Laboratory MASSIVE. Since 2003, he has been a member of the Eurographics Association. He is the President of the Portuguese Computer Graphics Chapter.(Based on document published on 16 June 2021).

Publications

65

Citations

569

Publications by Year

20102024

Co-Authors:

Telmo Adão
Vasco Amorim
Miguel Bacelar
Luís Barbosa
João Barreira

Show All Co-Authors (70)

Author's Published Works

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Showing 1-25 of 26 results

Conferences (16)

Journals (10)

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Alain Lalande(18)
A. Lalande(8)
F. Brunotte(6)
L. Legrand(6)
Pierre-Marc Jodoin(5)
ImViA EA 7535 laboratory, University of Burgundy, France(2)
LASEE laboratory, National Engineering School of Monastir, University of Monastir, Tunisia(2)
Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada(2)
MRI Department, University Hospital of Dijon, Dijon, France(2)
Department of Computer Engineering and Mathematics, Rovira i Virgili University, Tarragona, Spain(1)
IEEE Transactions on Medical Imaging(4)
2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems(2)
IEEE Journal of Biomedical and Health Informatics(2)
IEEE Transactions on Biomedical Engineering(2)
2007 Computers in Cardiology(1)
IEEE(26)
Media(3)
Dijon, France(2)
Paris, France(2)
Boston, MA, USA(1)
Cancun, Mexico(1)
Durham, NC, USA(1)
Magnetic Resonance Imaging(18)
Left Ventricular(13)
Cardiac Cycle(7)
Cardiovascular Magnetic Resonance(6)
Convolutional Neural Network(6)

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Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect

Leonardo Geronzi;Aline Bel-Brunon;Antonio Martinez;Michel Rochette;Marco Sensale;Olivier Bouchot;Alain Lalande;Siyu Lin;Pier Paolo Valentini;Marco Evangelos Biancolini

IEEE Transactions on Biomedical Engineering

Year: 2023 | Volume: 70, Issue: 11 | Journal Article |

Cited by: Papers (1)

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Objective: We propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. Methods: We first segment the TA from magneti...Show More

Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect

Leonardo Geronzi;Aline Bel-Brunon;Antonio Martinez;Michel Rochette;Marco Sensale;Olivier Bouchot;Alain Lalande;Siyu Lin;Pier Paolo Valentini;Marco Evangelos Biancolini

IEEE Transactions on Biomedical Engineering

Year: 2023 | Volume: 70, Issue: 11 | Journal Article |

A deep learning approach for the segmentation of myocardial diseases

Khawla Brahim;Abdul Qayyum;Alain Lalande;Arnaud Boucher;Anis Sakly;Fabrice Meriaudeau

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Cited by: Papers (2)

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Cardiac left ventricular (LV) segmentation is a paramount essential step for both diagnosis and treatment of cardiac pathologies such as ischemia, myocardial infarction, arrhythmia and myocarditis. However, this segmentation is challenging due to high variability across patients and the potential lack of contrast between structures. In this work, we propose and evaluate a (2.5D) SegU-Net model bas...Show More

A deep learning approach for the segmentation of myocardial diseases

Khawla Brahim;Abdul Qayyum;Alain Lalande;Arnaud Boucher;Anis Sakly;Fabrice Meriaudeau

2020 25th International Conference on Pattern Recognition (ICPR)

Year: 2021 | Conference Paper |

Learning With Context Feedback Loop for Robust Medical Image Segmentation

Kibrom Berihu Girum;Gilles Créhange;Alain Lalande

IEEE Transactions on Medical Imaging

Year: 2021 | Volume: 40, Issue: 6 | Journal Article |

Cited by: Papers (35)

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Deep learning has successfully been leveraged for medical image segmentation. It employs convolutional neural networks (CNN) to learn distinctive image features from a defined pixel-wise objective function. However, this approach can lead to less output pixel interdependence producing incomplete and unrealistic segmentation results. In this paper, we present a fully automatic deep learning method ...Show More

Learning With Context Feedback Loop for Robust Medical Image Segmentation

Kibrom Berihu Girum;Gilles Créhange;Alain Lalande

IEEE Transactions on Medical Imaging

Year: 2021 | Volume: 40, Issue: 6 | Journal Article |

A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases

Khawla Brahim;Abdul Qayyum;Alain Lalande;Arnaud Boucher;Anis Sakly;Fabrice Meriaudeau

2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)

Year: 2020 | Conference Paper |

Cited by: Papers (3)

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Accurate three-dimensional (3D) cardiac segmentation from late gadolinium enhancement (LGE)-MRI plays a critical role in designing a structure of reference for diagnosing many cardiac pathologies such as ischemia, myocarditis and myocardial infarction. This segmentation is however still a non-trivial task, due to the motion artifacts during acquisition, and heterogeneous intensity distributions. I...Show More

A 3D deep learning approach based on Shape Prior for automatic segmentation of myocardial diseases

Khawla Brahim;Abdul Qayyum;Alain Lalande;Arnaud Boucher;Anis Sakly;Fabrice Meriaudeau

2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA)

Year: 2020 | Conference Paper |

Myocardial Infarction Segmentation From Late Gadolinium Enhancement MRI By Neural Networks and Prior Information

Zhihao Chen;Alain Lalande;Michel Salomon;Thomas Decourselle;Thibaut Pommier;Gilles Perrot;Raphaël Couturier

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cited by: Papers (2)

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In this paper, we propose an automatic myocardial infarction segmentation framework from Delayed Enhancement cardiac MRI (DE-MRI) using a convolutional neural network (CNN) and prior information-based post-treatments. The work was conducted on our DE-MRI dataset, which is collected from daily clinical practice. 195 cases of DE-MRI examinations constitute this dataset, including on average 7 images...Show More

Myocardial Infarction Segmentation From Late Gadolinium Enhancement MRI By Neural Networks and Prior Information

Zhihao Chen;Alain Lalande;Michel Salomon;Thomas Decourselle;Thibaut Pommier;Gilles Perrot;Raphaël Couturier

2020 International Joint Conference on Neural Networks (IJCNN)

Year: 2020 | Conference Paper |

Cardiac Segmentation With Strong Anatomical Guarantees

Nathan Painchaud;Youssef Skandarani;Thierry Judge;Olivier Bernard;Alain Lalande;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2020 | Volume: 39, Issue: 11 | Journal Article |

Cited by: Papers (82)

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Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for produci...Show More

Cardiac Segmentation With Strong Anatomical Guarantees

Nathan Painchaud;Youssef Skandarani;Thierry Judge;Olivier Bernard;Alain Lalande;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2020 | Volume: 39, Issue: 11 | Journal Article |

Contribution of Augmented Reality to Minimally Invasive Computer-Assisted Cranial Base Surgery

Raabid Hussain;Alain Lalande;Caroline Guigou;Alexis Bozorg-Grayeli

IEEE Journal of Biomedical and Health Informatics

Year: 2020 | Volume: 24, Issue: 7 | Journal Article |

Cited by: Papers (20)

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Cranial base procedures involve manipulation of small, delicate and complex structures in the fields of otology, rhinology, neurosurgery and maxillofacial surgery. Critical nerves and blood vessels are in close proximity of these structures. Augmented reality is an emerging technology that can revolutionize the cranial base procedures by providing supplementary anatomical and navigational informat...Show More

Contribution of Augmented Reality to Minimally Invasive Computer-Assisted Cranial Base Surgery

Raabid Hussain;Alain Lalande;Caroline Guigou;Alexis Bozorg-Grayeli

IEEE Journal of Biomedical and Health Informatics

Year: 2020 | Volume: 24, Issue: 7 | Journal Article |

FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention

Vivek Kumar Singh;Mohamed Abdel-Nasser;Hatem A. Rashwan;Farhan Akram;Nidhi Pandey;Alain Lalande;Benoit Presles;Santiago Romani;Domenec Puig

Year: 2019 | Volume: 7 | Journal Article |

Cited by: Papers (55)

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Skin lesion segmentation in dermoscopic images is still a challenge due to the low contrast and fuzzy boundaries of lesions. Moreover, lesions have high similarity with the healthy regions in terms of appearance. In this paper, we propose an accurate skin lesion segmentation model based on a modified conditional generative adversarial network (cGAN). We introduce a new block in the encoder of cGAN...Show More

FCA-Net: Adversarial Learning for Skin Lesion Segmentation Based on Multi-Scale Features and Factorized Channel Attention

Vivek Kumar Singh;Mohamed Abdel-Nasser;Hatem A. Rashwan;Farhan Akram;Nidhi Pandey;Alain Lalande;Benoit Presles;Santiago Romani;Domenec Puig

Year: 2019 | Volume: 7 | Journal Article |

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

Clement Zotti;Zhiming Luo;Alain Lalande;Pierre-Marc Jodoin

IEEE Journal of Biomedical and Health Informatics

Year: 2019 | Volume: 23, Issue: 3 | Journal Article |

Cited by: Papers (139)

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In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limit...Show More

Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation

Clement Zotti;Zhiming Luo;Alain Lalande;Pierre-Marc Jodoin

IEEE Journal of Biomedical and Health Informatics

Year: 2019 | Volume: 23, Issue: 3 | Journal Article |

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Cited by: Papers (1177)

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Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large...Show More

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map

Jorge Zavala Bojorquez;Stéphanie Bricq;Paul M. Walker;Alain Lalande

2015 IEEE International Conference on Image Processing (ICIP)

Year: 2015 | Conference Paper |

Cited by: Papers (2)

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This paper presents a new methodology providing the first step towards generating attenuation maps for PET/MR systems based solely on MR information. The new method segments and classifies the attenuation-differing regions of the patient's pelvis based on acquired T1- and T2-weighted MR data sets and anatomical-based knowledge by computing the tissue specific T1 and T2 relaxation times, using a ro...Show More

Automatic classification of tissues using T1 and T2 relaxation times from prostate MRI: A step towards generation of PET/MR attenuation map

Jorge Zavala Bojorquez;Stéphanie Bricq;Paul M. Walker;Alain Lalande

2015 IEEE International Conference on Image Processing (ICIP)

Year: 2015 | Conference Paper |

Left-Ventricle Segmentation of SPECT Images of Rats

Pierre-Marc Jodoin;Fredy Pinheiro;Alexandra Oudot;Alain Lalande

IEEE Transactions on Biomedical Engineering

Year: 2015 | Volume: 62, Issue: 9 | Journal Article |

Cited by: Papers (5)

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Single-photon emission computed tomography (SPECT) imaging of the heart is helpful to quantify the left-ventricular ejection fraction and study myocardial perfusion scans. However, these evaluations require a 3-D segmentation of the left-ventricular wall on each phase of the cardiac cycle. This paper presents a fast and interactive graph cut method for 3-D segmentation of the left ventricle (LV) o...Show More

Left-Ventricle Segmentation of SPECT Images of Rats

Pierre-Marc Jodoin;Fredy Pinheiro;Alexandra Oudot;Alain Lalande

IEEE Transactions on Biomedical Engineering

Year: 2015 | Volume: 62, Issue: 9 | Journal Article |

A mutual reference shape based on information theory

S. Jehan-Besson;C. Tilmant;A. de Cesare;A. Lalande;A. Cochet;J. Cousty;J. Lebenberg;M. Lefort;P. Clarysse;R. Clouard;L. Najman;L. Sarry;F. Frouin;M. Garreau

2014 IEEE International Conference on Image Processing (ICIP)

Year: 2014 | Conference Paper |

Cited by: Papers (4)

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In this paper, we consider the estimation of a reference shape from a set of different segmentation results using both active contours and information theory. The reference shape is defined as the minimum of a criterion that benefits from both the mutual information and the joint entropy of the input segmentations and is then called a mutual shape. This energy criterion is here justified using sim...Show More

A mutual reference shape based on information theory

S. Jehan-Besson;C. Tilmant;A. de Cesare;A. Lalande;A. Cochet;J. Cousty;J. Lebenberg;M. Lefort;P. Clarysse;R. Clouard;L. Najman;L. Sarry;F. Frouin;M. Garreau

2014 IEEE International Conference on Image Processing (ICIP)

Year: 2014 | Conference Paper |

An Adapted Optical Flow Algorithm for Robust Quantification of Cardiac Wall Motion From Standard Cine-MR Examinations

Marie Xavier;Alain Lalande;Paul M. Walker;François Brunotte;Louis Legrand

IEEE Transactions on Information Technology in Biomedicine

Year: 2012 | Volume: 16, Issue: 5 | Journal Article |

Cited by: Papers (27)

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This paper presents a method for local myocardial motion estimation from a conventional steady-state free precession cine-MRI sequence using a modified phase-based optical flow (OF) technique. Initially, the technique was tested on synthetic images to evaluate its robustness with regards to Rician noise and to brightness variations. The method was then applied to cardiac images acquired on 11 heal...Show More

An Adapted Optical Flow Algorithm for Robust Quantification of Cardiac Wall Motion From Standard Cine-MR Examinations

Marie Xavier;Alain Lalande;Paul M. Walker;François Brunotte;Louis Legrand

IEEE Transactions on Information Technology in Biomedicine

Year: 2012 | Volume: 16, Issue: 5 | Journal Article |

Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

Jessica Lebenberg;Irène Buvat;Alain Lalande;Patrick Clarysse;Christopher Casta;Alexandre Cochet;Constantin Constantinides;Jean Cousty;Alain de Cesare;Stéphanie Jehan-Besson;Muriel Lefort;Laurent Najman;Elodie Roullot;Laurent Sarry;Christophe Tilmant;Mireille Garreau;Frédérique Frouin

IEEE Transactions on Medical Imaging

Year: 2012 | Volume: 31, Issue: 8 | Journal Article |

Cited by: Papers (26)

A statistical methodology is proposed to rank several estimation methods of a relevant clinical parameter when no gold standard is available. Based on a regression without truth method, the proposed approach was applied to rank eight methods without using any a priori information regarding the reliability of each method and its degree of automation. It was only based on a prior concerning the stat...Show More

Nonsupervised Ranking of Different Segmentation Approaches: Application to the Estimation of the Left Ventricular Ejection Fraction From Cardiac Cine MRI Sequences

Jessica Lebenberg;Irène Buvat;Alain Lalande;Patrick Clarysse;Christopher Casta;Alexandre Cochet;Constantin Constantinides;Jean Cousty;Alain de Cesare;Stéphanie Jehan-Besson;Muriel Lefort;Laurent Najman;Elodie Roullot;Laurent Sarry;Christophe Tilmant;Mireille Garreau;Frédérique Frouin

IEEE Transactions on Medical Imaging

Year: 2012 | Volume: 31, Issue: 8 | Journal Article |

A 4D Patient-Specific Modelling of the Thoracic Aorta from Cine-MR Images

Rocio Cabrera Lozoya;Olivier Bouchot;Tadeusz Sliwa;Eric Steinmetz;Yvon Voisin;Alain Lalande

2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems

Year: 2011 | Conference Paper |

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Current indications for aortic surgery are solely based on maximum aortic diameter and have proven to be nonreliable. There is an urgent need of pertinent information to aid physicians assess surgical risk-benefits and develop an adequate treatment for the patient as the morbidity and mortality risks for these interventions are considerably high. In this paper, we present an algorithm that semi-au...Show More

A 4D Patient-Specific Modelling of the Thoracic Aorta from Cine-MR Images

Rocio Cabrera Lozoya;Olivier Bouchot;Tadeusz Sliwa;Eric Steinmetz;Yvon Voisin;Alain Lalande

2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems

Year: 2011 | Conference Paper |

Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI

Vanya V. Valindria;Marion Angue;Nicolas Vignon;Paul M. Walker;Alexandre Cochet;Alain Lalande

2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems

Year: 2011 | Conference Paper |

Cited by: Papers (8)

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In this study, we developed a novel method for a fullyautomatic segmentation and quantification of myocardialinfarction from delay enhancement - magnetic resonanceimaging. Hyper-enhanced region corresponding to the infarctcore is separated from normal tissue via a Gaussian mixturemodel. Then, peri-infarct area is determined by spatial-weightedfuzzy clustering. No-reflow area inside the infarct cor...Show More

Automatic Quantification of Myocardial Infarction from Delayed Enhancement MRI

Vanya V. Valindria;Marion Angue;Nicolas Vignon;Paul M. Walker;Alexandre Cochet;Alain Lalande

2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems

Year: 2011 | Conference Paper |

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences

Jessica Lebenberg;Irène Buvat;Mireille Garreau;Christopher Casta;Constantin Constantinidès;Jean Cousty;Alexandre Cochet;Stéphanie Jehan-Besson;Christophe Tilmant;Muriel Lefort;Elodie Roullot;Laurent Najman;Laurent Sarry;Patrick Clarysse;Alain de Cesare;Alain Lalande;Frédérique Frouin

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Cited by: Papers (3)

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A statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliab...Show More

Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction from cardiac cine MRI sequences

Jessica Lebenberg;Irène Buvat;Mireille Garreau;Christopher Casta;Constantin Constantinidès;Jean Cousty;Alexandre Cochet;Stéphanie Jehan-Besson;Christophe Tilmant;Muriel Lefort;Elodie Roullot;Laurent Najman;Laurent Sarry;Patrick Clarysse;Alain de Cesare;Alain Lalande;Frédérique Frouin

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Year: 2011 | Conference Paper |

Markovian method for 2D, 3D and 4D segmentation of MRI

Pierre-Marc Jodoin;Alain Lalande;Yvon Voisin;Olivier Bouchot;Eric Steinmetz

2008 15th IEEE International Conference on Image Processing

Year: 2008 | Conference Paper |

Cited by: Papers (2)

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Magnetic resonance imaging (MRI) is well adapted for early detection of diseases such as aortic aneuryms or dissections. In this paper, we present a new Markovian method which evolves an active contour for 2D, 3D and 4D (3D + time) segmentation. As opposed to other Markovian contour-based methods, our approach considers an implicit contour as the boundary of a 2D region. The regions are modeled vi...Show More

Markovian method for 2D, 3D and 4D segmentation of MRI

Pierre-Marc Jodoin;Alain Lalande;Yvon Voisin;Olivier Bouchot;Eric Steinmetz

2008 15th IEEE International Conference on Image Processing

Year: 2008 | Conference Paper |

Dynamic 4D blood flow representation in the aorta and analysis from cine-MRI in patients

M. Xavier;A. Lalande;P.M. Walker;C. Boichot;A. Cochet;O. Bouchot;E. Steinmetz;L. Legrand;F. Brunotte

2007 Computers in Cardiology

Year: 2007 | Conference Paper |

Cited by: Papers (1)

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Natural evolution of aortic disease is characterized by a diameter increase that can result in aortic dissection or rupture. Currently the evaluation of risk of rupture or dissection is based on the size of the aorta. However, this parameter is not always relevant and it appears necessary to define new parameters.Show More

Dynamic 4D blood flow representation in the aorta and analysis from cine-MRI in patients

M. Xavier;A. Lalande;P.M. Walker;C. Boichot;A. Cochet;O. Bouchot;E. Steinmetz;L. Legrand;F. Brunotte

2007 Computers in Cardiology

Year: 2007 | Conference Paper |

Automatic detection of vessel wall contours from cine-MRI for aortic compliance determination

J.L. Rose;A. Lalande;E. Bourennane;P.M. Walker;O. Bouchot;E. Steinmetz;L. Legrand;Y. Voisin;J.E. Wolf;F. Brunotte

Computers in Cardiology, 2005

Year: 2005 | Conference Paper |

Cited by: Papers (5)

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The aortic compliance is defined as the relative change of aortic cross-sectional area divided by the change in arterial pressure. Magnetic resonance imaging (MRI) can be used to evaluate aortic compliance. A knowledge of the aortic contour is essential to determine the aortic area. To prevent important intra-and inter-observer variability, the aortic contours are detected automatically. This work...Show More

Automatic detection of vessel wall contours from cine-MRI for aortic compliance determination

J.L. Rose;A. Lalande;E. Bourennane;P.M. Walker;O. Bouchot;E. Steinmetz;L. Legrand;Y. Voisin;J.E. Wolf;F. Brunotte

Computers in Cardiology, 2005

Year: 2005 | Conference Paper |

Automatic realignment of myocardial first-pass MR perfusion images

A. Comte;A. Lalande;F. Brunotte

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the

Year: 2003 | Conference Paper |

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Magnetic resonance first-pass imaging of a bolus of contrast agent is well adapted to distinguish normal and hypoperfused areas of the myocardium. In most cases, the signal intensity-time curves in user defined regions of interest (ROI) are studied. As image acquisition is ECG-gated, the images are acquired at the same moment in the cardiac cycle, and the basic shape of the heart is similar from o...Show More

Automatic realignment of myocardial first-pass MR perfusion images

A. Comte;A. Lalande;F. Brunotte

3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the

Year: 2003 | Volume: 2 | Conference Paper |

Object tracking in medical imaging using a 2D active mesh system

J. Lautissier;L. Legrand;A. Lalande;P. Walker;F. Brunotte

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)

Year: 2003 | Conference Paper |

Cited by: Papers (3)

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This article proposes a technique for tracking moving organs in medical imaging. It can be split into two stages. We first initialize a 1D-triangular mesh on the first image of the sequence. We distinguish different objects of interest by grouping together the triangles that make them up. Afterwards, we deform this mesh on the successive images in order to track each identified object. The trackin...Show More

Object tracking in medical imaging using a 2D active mesh system

J. Lautissier;L. Legrand;A. Lalande;P. Walker;F. Brunotte

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439)

Year: 2003 | Volume: 1 | Conference Paper |

Magnetic resonance image segmentation and heart motion tracking with an active mesh based system

L. Legrand;C. Bordier;A. Lalande;P. Walker;F. Brunotte;C. Quantin

Computers in Cardiology

Year: 2002 | Conference Paper |

Cited by: Papers (2)

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The work presented here relates to a method for motion tracking in sequences of medical images. The purpose is to quantify the general motions and the local deformations of a beating heart during a cardiac cycle. In order to achieve this goal, we first tessellate the first image of the sequence into triangular patches. A Delaunay triangulation is applied to find the optimal set of triangles descri...Show More

Magnetic resonance image segmentation and heart motion tracking with an active mesh based system

L. Legrand;C. Bordier;A. Lalande;P. Walker;F. Brunotte;C. Quantin

Computers in Cardiology

Year: 2002 | Conference Paper |

Cardiac motion tracking using a deformable 2D-mesh modeling

E. Desseree;L. Legrand;P. Walker;A. Lalande;F. Brunotte

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)

Year: 2001 | Conference Paper |

Cited by: Papers (1)

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The work reported here deals with movement tracking in sequences of medical images in order to quantify the general movements and deformations of the heart. For this purpose, we partition the first image into triangular patches in order that each object of the image corresponds to a set of triangles. Then, the nodes of the mesh are tracked across the image sequence giving a mesh which warps with t...Show More

Cardiac motion tracking using a deformable 2D-mesh modeling

E. Desseree;L. Legrand;P. Walker;A. Lalande;F. Brunotte

Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205)

Year: 2001 | Volume: 2 | Conference Paper |

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