Analysis of cardiac function in standing and supine postures using Gravity MRI

・Ohno Naoki, Miyati Toshiaki, Imao Shoki, Nakagawa Seiya

Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI

・François Legrand, Richard Macwan, Alain Lalande, ORCID, Lisa Métairie and Thomas Decourselle

Tracking of Blood Vessels Motion from 4D-flow MRI Data

・Mocia Agbalessi, Alain Lalande, Olivier Bouchot, Toshiyuki Hayase , Jean-Joseph Christophe , Miguel Angel Fernández and Damiano Lombardi

Segmentation of 4D Flow MRI: Comparison between 3D Deep Learning and Velocity-Based Level Sets

・Armando Barrera-Naranjo ,Diana M. Marin-Castrillon ,Thomas Decourselle ,Siyu Lin ,Sarah Leclerc ,Marie-Catherine Morgant ,Chloé Bernard ,Shirley De Oliveira , Arnaud Boucher, Benoit Presles , Olivier Bouchot , Jean-Joseph Christophe and Alain Lalande

Automatic classification of patients with myocardial infarction or myocarditis based only on clinical data: A quick response

Sheikh Shah Mohammad Motiur Rahman Zhihao ChenAlain Lalande Thomas Decourselle Alexandre Cochet Thibaut PommierYves Cottin Michel Salomon Raphaël Couturier 

GANs for Medical Image Synthesis: An Empirical Study

Youssef Skandarani, Pierre-Marc Jodoin and Alain Lalande


Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing

・Emmanouil Markodimitrakis , Siyu Lin , Emmanouil Koutoulakis , Diana Marcela Marín-Castrillón , Francisco Aarón Tovar Sáez , Sarah Leclerc, Chloé Bernard , Arnaud Boucher , Benoit Presles  , Olivier Bouchot , Thomas Decourselle , Marie-Catherine Morgant , andAlain Lalande

Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist

Qayyum A, Lalande A, Meriaudeau F

Automated segmentation of normal and diseased coronary arteries – The ASOCA challenge

・Ramtin GharleghiDona AdikariKaty EllenbergerSze-YuanOoiChris Ellis,Chung-MingChenRuochen GaoYuting HeRaabid Hussain,Chia-YenLeeJun Li, Jun MaZiwei NieBruno OliveiraYaolei QiYoussef SkandaraniJoão L.VilaçaXiyue WangSusannBeier

Deep learning methods for automatic evaluation of delayed enhancement-MRI.
The results of the EMIDEC challenge

・Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon,Dominique Ginhac, Youssef Skandarani, Arnaud Boucher,Khawla Brahim,  Marleende Bruijne, Robin Camarasa, Teresa M.Correia, Xue Feng, Kibrom B.Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain…Fabrice Meriaudeau

Prediction of Myocardial Infarction From Patient Features With Machine Learning

・Zhihao Chen, Jixi Shi, Thibaut Pommier, Yves Cottin, Michel Salomon, Thomas Decourselle, Alain Lalande and Raphaël Couturier


A 3D network based shape prior for automatic myocardial disease segmentation in delayed-enhancement MRI

K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau

A deep learning approach for the segmentation of myocardial diseases

K Brahim, A Qayyum, A Lalande, A Boucher, A Sakly, F Meriaudeau

Efficient 3D Deep Learning for Myocardial Diseases Segmentation.

K. Brahim, A. Qayyum, A. Lalande, A. Boucher, A. Sakly, F. Meriaudeau.

Automatic deep learning-based myocardial infarction segmentation from delayed enhancement MRI

Zhihao Chen, Alain Lalande, Michel Salomon, Thomas Decourselle, Thibaut Pommier, Abdul Qayyum, Jixi Shi, Gilles Perrot, Raphaël Couturier

Myocardial Infarction Quantification From Late Gadolinium Enhancement MRI Using Top-hat Transforms and Neural Networks

・Ezequiel de la Rosa, Désiré Sidibé, Thomas Decourselle, Thibault Leclercq, Alexandre Cochet, Alain Lalande

Emidec: A Database Usable for the Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI

Alain Lalande, Zhihao Chen, Thomas Decourselle, Abdul Qayyum, Thibaut Pommier, Luc Lorgis, Ezequiel de la Rosa, Alexandre Cochet, Yves Cottin, Dominique Ginhac, Michel Salomon, Raphaël Couturier,Fabrice Meriaudeau


Automatic segmentation of tumors and affected organs in the abdomen using a 3D hybrid model for computed tomography imaging

Qayyum A, Lalande A, Meriaudeau F

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

K. Brahim, A. Qayyum, A. Lalande, A. Boucher, A. Sakly, F. Meriaudeau

Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges: 11th International Workshop, STACOM 2020

EP Anton, M Pop, M Sermesant, V Campello, A Lalande, K Lekadir, A Suinesiaputra, O Camara, A Young

Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks

・Kibrom Berihu Girum, Youssef Skandarani, Raabid Hussain, Alexis Bozorg Grayeli, Gilles Créhange, Alain Lalande

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 and Raphael Couturier

・Youssef Skandarani, Nathan Painchaud, Pierre-Marc Jodoin, Alain Lalande

Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model, MIDL 2020 conference.

Qayyum A., Lalande A., Decourselle T., Pommier T., Cochet A., and Meriaudeau F., 



・Yuki Hori1, Suguru Miyauchi2, Toshiyuki Hayase2, Alain Lalande3, and Jean-Joseph Christophe4

Cardiac MRI Segmentation with Strong Anatomical Guarantee.

N. Painchaud, Y. Skandarani, T. Judge, O. Bernard, A. Lalande and P. -M. Jodoin, « Cardiac Segmentation With Strong Anatomical Guarantees, » in IEEE Transactions           on Medical Imaging, vol. 39, no. 11, pp. 3703- 3713, Nov. 2020, doi: 10.1109/TMI.2020.3003240.


Automatic determination of aortic compliance based on MRI and adapted curvilinear detector.

・ J Mitéran, O Bouchot, A Cochet, A Lalande. Biomedical Signal Processing and Control 40, 295-311,2018


Evolution of cardiac magnetic resonance late gadolinium enhancement extent and clinical events
after infarct-like acute myocarditis.

・ T Pommier, S Tisserand, C Guenancia, T Leclercq, F Bichat, A Lalande, M. Zeller A. Cochet Y. Cottin. European Heart Journal 38 (suppl_1),2017


Aortic Function’s Adaptation in Response to Exercise-Induced Stress Assessing by 1.5T MRI.

・ L. Bal-Theoleyre, A. Lalande, R. Giorgi, F. Collart, P. Piquet, G. Habib, J.F. Avierinos, M. Bernard, M. Guye, A. Jacquier. : A Pilot Study in Healthy
Volunteers. PLoS One. 2016; 11(6). Doi: 10.137.


An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard
cine-MR examinations.

・ M. Xavier, A. Lalande, P.M. Walker, F. Brunotte, L. Legrand. IEEE Trans InfTechnol Biomed. 2012; 16(5): 859-868. L. Bal-Theoleyre, A. Lalande, R. Giorgi, F. Collart, P. Piquet, G. Habib, J.F. Avierinos, M. Bernard, M. Guye, A. Jacquier. : A Pilot Study in Healthy
Volunteers. PLoS One. 2016; 11(6). Doi: 10.137.


Influence of age and sex on aortic distensibility assessed by MRI in healthy subjects.

・ J.L. Rose, A. Lalande, O. Bouchot, E.-B. Bourennane, P.M. Walker, P. Ugolini, C. Revol-Muller, R. Cartier, F. Brunotte. Magn Reson Imaging,
2010 ; 28(2) : 255-263.


Compliance and pulse wave velocity assessed by MRI detect early aortic impairment in young
patients with mutation of the smooth muscle myosin heavy chain.

・ A. Lalande, P. Khau Van Kien, P.M. Walker, L. Zhu, L. Legrand, M. Claustres, X. Jeunemaître, F. Brunotte, J.-E. Wolf. J Magn
Reson Imaging, 2008; 28 (5): 1180-1187.

Utility of Cardiac Magnetic Resonance to assess association between admission hyperglycemia and
myocardial damage in patients with reperfused ST-segment elevation myocardial infarction.

・ Cochet, M. Zeller, A. Lalande, I. L’huillier, P.M. Walker, C. Touzery, B. Verges, J.-E. Wolf, F. Brunotte, Y. Cottin.J CardiovascMagnReson, 10(1):2, 2008.


Automatic fuzzy classification of the washout curves from MR first-pass perfusion imaging after
myocardial infarction.

・ A. Comte, A. Lalande, A. Cochet, P.M. Walker, J.-E. Wolf, Y. Cottin, F. Brunotte. Invest. Radiol., 2005 ; 40 (8) : 545-555.

Mapping of familial thoracic aortic aneurysm / dissection with patent ductusarteriosus to 16p12.2-

・ P. Khau van Kien, F. Mathieu, L. Zhu, A. Lalande, C. Betard, M. Lathrop, F. Brunotte, J.-E. Wolf, X. Jeunemaitre. Circulation, 2005; 112 : 200-206.
Lyon, France; 2005: 411-414.

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

J.-L. Rose, A. Lalande, P.M. Walker, O. Bouchot, E. Steinmetz, L. Legrand, Y. Voisin, J.-E. Wolf, F. Brunotte. Automatic detection of vessel wall contours from cine-MRI for aortic compliance determination. I.E.E.E. Computers In Cardiology: Lyon, France; 2005: 411-414.


Visual estimation of the global myocardial extent of hyperenhancement on delayed contrastenhanced

・ A. Comte, A. Lalande, P.M. Walker, A. Cochet, L. Legrand, J.E. Wolf, F. Brunotte. EurRadiol, 2004; 14(12):2182-2187.

Left ventricular ejection fraction calculation from automatically selected and processed diastolic and
systolic frames in short axis cine-MRI .

・ A. Lalande, N. Salvé, A. Comte, M.-C. Jaulent, L. Legrand, P.M. Walker, Y. Cottin, J.E. Wolf, F. Brunotte. J CardiovascMagnReson, 2004;

Realignment of myocardial first-pass MR perfusion images using an automatic detection of the heartlung interface.

・ A. Comte, A. Lalande, S. Aho, P.M. Walker, F. Brunotte. MagnReson Imaging, 2004; 22(7):1001-1009.


Early study of myocardial perfusion with MRI in revascularized infarcts.

・ K. Rezaizadeh-Bourdariat, A. Lalande, D. Ben Salem, A. Comte, N. Salve, S. Aho, P.M. Walker, F. Brunotte, Y. Cottin, J.E Wolf.Arch Mal CoeurVaiss,
2003; 96(1):30-34


Automatic determination of aortic compliance with cine-magnetic resonance imaging: an application
of fuzzy logic theory.

・ A. Lalande, P. Khau van Kien, N. Salve, D. Ben Salem, L. Legrand, P.M. Walker, J.E. Wolf, F. Brunotte. Invest. Radiol., 2002; 37 (12): 685-691.


Automatic detection of left ventricular contours from cardiac cine-MRI using fuzzy logic.Invest.

・ A. Lalande, L. Legrand, P. M. Walker, F. Guy, Y. Cottin, S. Roy, F. Brunotte., 1999; 34 : 211-217.