Su Ruan

photo2010-6

su.ruan(AT) univ-rouen.fr

Professeur à l’Université de Rouen, France

(Full Professor at the University of Rouen, France)

Formations et Diplômes (Education and  Diplomas

Jan. 1993           Thèse de doctorat (Laboratoire de Traitement du Signal et de l’Image (LTSI)), soutenue à l’Université de Rennes I.

                               (Ph.D. , University of Rennes I, France, Jan. 1993.)

Déc. 2000           Habilitation à Diriger des Recherches (HDR) (Labo. GREYC UMR 6072 CNRS), soutenue à l’Université de Caen.

                               (Professorship Diploma HDR, University of  Caen, Dec. 2000.)

 Activités professionnelles (Professional Activities)

 1992 – 1993      ATER à l’INSA de Rennes.

                              (Assistant professor at  the National Institute for Applied Sciences of  Rennes, France)

 1993 – 2003      Maître de conférences à l’Université de Caen.

                               (Associate professor at the Univesity of Caen, France)

 2003 – 2010      Professeur à l’IUT de Troyes de l’Université de Reims Champagne-Ardenne.

                               (Full professor at the University of Reims Champagne-Ardenne, France)

 Depuis 2010      Professeur à l’Université de Rouen.

                               (Full professor at the University of Rouen since 2010, France)

Thèmes de recherche développés (Research fields)

a) La segmentation d’images et la reconnaissance de formes ( Image segmentation and Pattern Recognition)

Segmentation : champs aléatoires de Markov, contours actifs (level sets), modèles de forme statistiques, recalage non rigide, méthodes basées sur les graphes.

(Segmentation :  Markov random fields,  Active contours (level sets),  statistical shape models, non-rigid registration, Graph based methods. )

Reconnaissance de formes : Modélisation des connaissances a priori par des champs flous. Modèles de formes statistiques à partir d’un  ensemble d’apprentissage sur les formes 3D. Classification par apprentissage statistique. Sélection des caractéristiques.

(Pattern Recognition: A priori knowledge modeling by fuzzy fields. Statistical shape models. Deep learning. Dictionary learning. Feature selection.)

b) La fusion des informations (Information fusion)

Fusion floue des informations modélisées par des ensembles flous. Fusion basée la sélection des caractéristiques, Fusion basée sur la théorie des fonctions de croyance.

(Fuzzy fusion, Fusion-based on  feature selection, Fusion based on the theory of belief functions.)

c) Applications (Applications)

– Imageries médicales: IRM cérébrale, IRM prostatique, Spectroscopie par RM (SRM) , PET-TDM thoracique

  ( Medical imaging: brain MRI, Prostate MRI, MR spectroscopy (MRS), thoracic PET-CT)

– Segmentation et classification des images médicales

   (Segmentation and classification of  medical images for diagnostic and therapy)

– Suivi longitudinal et sélection des caractéristiques prédictives pour le traitement du cancer .

    (Longitudinal follow-up and selection of predictive characteristics for cancer therapy.)

 

Publications

https://scholar.google.fr/citations?user=mjB2a6MAAAAJ&hl=fr

Publications principales depuis 2010

( Major publications since 2010)

Chapitre d’ouvrage (Book chapter)

Yu Guo, Su Ruan, “Signal Separation with A Priori Knowledge Using Sparse Representation”, In Amitava Chatterjee, Hadi Nobahari,and Patrick Siarry, editors, Advances in Heuristic Signal Processing and Applications, pp 315-332, Springer, 2013.

Revues internationales avec comité de lecture (International Journals)

  1. Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen, “Medical Image Synthesis with Deep Convolutional Adversarial Networks”, IEEE Transactions on Biomedical Engineering, in press, 2018. DOI:10.1109/TBME.2018.2814538
  2. Jian Wu, Thomas R. Mazur, Su Ruan, Chunfeng Lian, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Mark A. Anastasio, H. Michael Gach, Sasa Mutic, Maria Thomas, Hua Li, “A Deep Boltzmann Machine-Driven Level Set Method for Heart Motion Tracking Using Cine MRI”, Elsevier, Medical Image Analysis, Volume 47, Pages 68–80, July 2018. DOI: https://doi.org/10.1016/j.media.2018.03.015.
  3. Fan Wang, Chunfeng Lian, Pierre Vera, Su Ruan, “Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG–PET images”, Springer, Multimedia Systems, in press, 2018. DOI: https://doi.org/10.1007/s00530-017-0579-0
  4. Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, Pierre Vera, “Spatial Evidential Clustering with Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images”, IEEE. Trans. on Biomedical Engineering,Volume: 65, Issue: 1, pp. 21 – 30, Jan. 2018 . DOI: 10.1109/TBME.2017.2688453
  5. Jérome Lapuyade-Lahorgue, Jing-Hao Xue, Su Ruan, « Segmenting Multi-Source images using hidden Markov fields with copula-based multivariate statistical distributions », IEEE Transactions on Image Processing. Volume: 26, Issue: 7, pp: 3187-3195, July 2017. doi: 10.1109/TIP.2017.2685345
  6. Desbordes Paul, Ruan Su, Modzelewski Romain, Vauclin Sébastien, Vera Pierre, Gardin Isabelle, “Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier”, Elsevier, Computerized Medical Imaging and Graphics, Volume 60, Pages 42-49, September  2017. http://dx.doi.org/10.1016/j.compmedimag.2016.12.002
  7. Paul Desbordes, Su Ruan, Romain Modzelewski, Pascal Pineau, Sébastien Vauclin, Pierrick Gouel, Pierre Michel, Frédéric Di Fiore, Pierre Vera, Isabelle Gardin, « Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier », PloS one, Vol.12(3), March 2017. http://dx.doi.org/10.1371/journal.pone.0173208
  8. Anouan K. J., Lelandais B., Edet-Sanson A., Ruan S., Vera P., Gardin I., Hapdey S, « 18F-FDG-PET Partial volume effect correction using a modified recovery coefficient approach based on functional volume and local contrast: physical validation and clinical feasibility in oncology », The Quarterly Journal of Nuclear Medicine and Molecular Imaging, Vol 61 (3), pp: 301-313, September, 2017. DOI: 10.23736/S1824-4785.17.02756-X
  9. Chunfeng Lian, Su Ruan, and Thierry Denoeux « Dissimilarity Metric Learning in the Belief Function Framework », IEEE Transactions on Fuzzy Systems,  Volume: 24, Issue: 6, pp. 1555 – 1564,  Dec. 2016 . doi:10.1109/TFUZZ.2016.2540068.
  10. Chunfeng Lian, Su Ruan, Thierry Denœux, Fabrice Jardin, Pierre Vera, « Selecting Radiomic Features from FDG-PET Images for Cancer Treatment Outcome Prediction », Elsevier, Medical Image Analysis, Volume 32  Pages 257–268, August 2016. doi:10.1016/j.media.2016.05.007.
  11. Damien Grosgeorge, Caroline Petitjean, and Su Ruan, « A multilabel statistical shape prior for image segmentation », IET Image Processing, Vol.10 (10), Pages 710-716,  2016. DOI:  10.1049/iet-ipr.2015.0408.
  12. Paul Desbordes, Caroline Petitjean, Su Ruan, « Segmentation of lymphoma tumor in PET images using cellular automata: A preliminary study », Elsevier, IRBM, Volume 37, Issue 1, Pages 3–10,  2016. doi:10.1016/j.irbm.2015.11.001.
  13. Hua Li, Hsin-Chen Chen, Steven Dolly, Harold Li, Benjamin Fischer-Valuck, James Victoria, James Dempsey, Su Ruan, Mark Anastasio, Thomas Mazur, Michael Gach, Rojano Kashani, Olga Green, Vivian Rodriguez, Hiram Gay, Wade Thorstad, Sasa Mutic, “An integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy”,  Medical Physics, Vol. 43 (8), pp. 4700-4710, August 2016. DOI: 10.1118/1.4955118
  14. Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan, « Robust Feature Selection to Predict Tumor Treatment Outcome », Elsevier, Artificial Intelligence in Medicine, Volume 64, Issue 3,  Pages 195–204, July 2015.  doi:10.1016/j.artmed.2015.07.002
  15. Hongmei Mi, Caroline Petitjean, Pierre Vera, Su Ruan, « Joint Tumor Growth Prediction and Tumor Segmentation on Therapeutic Follow-up PET Images », Elsevier, Medical Image Analysis, Volume 23, Issue 1, Pages 84–91, July 2015. doi:10.1016/j.media.2015.04.016
  16. Chunfeng Lian, Su Ruan, Thierry Denœux, « An evidential classifier based on feature selection and two-step classification strategy », Elsevier, Pattern Recognition, Volume 48, Issue 7, Pages 2318–2327, July 2015. doi:10.1016/j.patcog.2015.01.019
  17. Caroline Petitjean, Maria A. Zuluaga, Wenjia Bai, Jean-NicolasDacher, Damien Grosgeorge, Jérôme Caudron, Su Ruan,, Ismail Ben Aye, M. Jorge Cardoso, Hsiang-Chou Chen, Daniel Jimenez-Carretero, Maria J. Ledesma-Carbayo, Christos Davatzikos, Jimit Doshi, Guray Erus, Oskar M.O. Maier, Cyrus M.S. Nambakhshi, Yangming Ouj, Sébastien Ourselin, Chun-Wei Peng, Nicholas S. Peters, Terry M.Peters, Martin Rajchl, Daniel Rueckert, Andres Santos, Wenzhe Shi, Ching-Wei Wang, Haiyan Wang, Jing Yuan, « Right Ventricle Segmentation From Cardiac MRI: A Collation Study », Elsevier, Medical Image Analysis, Volume 19, Issue 1, Pages 187–202, January 2015.  doi:10.1016/j.media.2014.10.004
  18. Pierre Buyssens, IsabelleGardin, Su Ruan, Abderrahim Elmoataz, « Eikonal-based region growing for efficient clustering », Elsevier, Image and Vision Computing,  Vol. 32 (12),  Pages 1045–1054, December 2014.  doi:10.1016/j.imavis.2014.10.002
  19. D.P. Onoma, S. Ruan, S. Thureau, L. Nkhali, R. Modzelewski, G.A. Monnehan, P. Vera, I. Gardin, « Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-Locally Adaptive Random Walk algorithm », Elsevier, Computerized Medical Imaging and Graphics,Volume 38, Issue 8, Pages 753–763, December 2014.  doi:10.1016/j.compmedimag.2014.09.007
  20. Benoit Lelandais, Su Ruan, Thierry Denoeux, Pierre Vera, Isabelle Gardin, « Fusion of multi-tracer PET images for Dose Painting », Elsevier, Medical Image Analysis, Volume 18, Issue 7, Pages 1247–1259, October 2014.   doi:10.1016/j.media.2014.06.014
  21. Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan, « Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients with PET »,  IEEE Transaction on Medical Imaging, Volume: 33 (4), pp: 995-1003, 2014.  10.1109/TMI.2014.230189
  22. Laurent D. Cohen, Khalifa Djemal, Su Ruan, Christine Toumoulin, Special Issue on biomedical image segmentation using variational and statistical approaches, Elsevier, IRBM, Volume 35 (1), pp: 1-2, February, 2014.  doi:10.1016/j.irbm.2014.02.001
  23. Pierre  Buyssens,  Isabelle Gardin, Su. Ruan, « Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images », Elsevier, IRBM, Volume 35 (1), pp:20-26, 2014.  doi:10.1016/j.irbm.2013.12.007
  24. B. Dubray, S. Thureau, L. Nkhali, R. Modzelewski, K. Doyeux, S. Ruan, P. Vera, « FDG-PET imaging for radiotherapy target volume definition in lung cancer », Elsevier, IRBM, Volume 35 (1), pp:41-45, 2014.  doi:10.1016/j.irbm.2013.12.008
  25. Benoit Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan, « Dealing with uncertainty and imprecision in image segmentation using belief function theory », Elsevier, International Journal of Approximate Reasoning, Volume 55, Issue 1, Part 3, Pages 376-387, January 2014.  doi:10.1016/j.ijar.2013.10.006
  26. Ines Ketata, Lamia Sallemi, Frédéric Morain-Nicolier Mohamed Ben Slima, Alexandre Cochet, Khalil Chtourou, Su Ruan & Ahmed Ben Hamida, « Factor Analysis-based Approach for Early Uptake Automatic Quantification of Breast Cancer by 18F-FDG PET Image Sequence », Elsevier, Biomedical Signal Processing and Control, Vol.9. pp.19–31, 2014.  doi:10.1016/j.bspc.2013.07.008
  27. Damien Grosgeorge, Caroline Petitjean, Bernard Dubray, and Su Ruan,  » Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut », Computational and Mathematical Methods in Medicine, Volume 2013, Article ID 547897, 6 pages, 2013.  http://dx.doi.org/10.1155/2013/547897
  28. D. Grosgeorge, C. Petitjean, J.-N. Dacher, S. Ruan, « Graph cut segmentation with a statistical shape model in cardiac MRI », Elsevier, Computer Vision and Image Understanding, Vol.117, pp.1027-1035, 2013.  doi:10.1016/j.cviu.2013.01.014
  29. P. Vera, R. Modzelewski, S. Hapdey, P. Gouel, H. Tilly, F. Jardin, S. Ruan, et I. Gardin. « Does enhanced CT influence the biological GTV measurement on FDG-PET images? », Elsevier, Radiotherapy and Oncology, 108: 86–90, 2013. doi:10.1016/j.radonc.2013.03.024
  30. XiangBo Lin, Su Ruan, Tian Shuang Qiu and DongMei Guo, « Non-rigid Medical Image Registration Based on Mesh Deformation Constraints », Computational and Mathematical Methods in Medicine, Volume 2013, Article ID 373082, 8 pages, 2013.    http://dx.doi.org/10.1155/2013/373082
  31. N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao and Y. Zhu, « Kernel Feature Selection to Fuse Multi-spectral MRI Images for Brain Tumor Segmentation », Elsevier, Computer Vision and Image Understanding, Vol.115 (2), pp.256-269, 2011.  doi:10.1016/j.cviu.2010.09.007
  32. Y. Guo, S. Ruan, J. Landré et P. Walker, « A Priori Knowledge Based Frequency-domain Quantification of Prostate Magnetic Resonance Spectroscopy », Elsevier, Biomedical Signal Processing and Control, vol.6(1), pp.13-20, 2011.  doi:10.1016/j.bspc.2013.07.008
  33. X. Lin, T. Qiu, F. Morain-Nicolier, S. Ruan, « A Topology Preserving Non-Rigid Registration Algorithm with Integration Shape Knowledge to Segment Brain Subcortical Structures from MRI Images », Elsevier, Pattern Recognition, Vol.43(7), pp.2418-2427, 2010.  doi:10.1016/j.patcog.2010.01.012
  34. Y. Guo, S. Ruan, J. Landré, J-M. Constans, « A Sparse Representation Method for Magnetic Resonance Spectroscopy Quantification », IEEE Transactions on Biomedical Engineering, 57(7):1620-1627, 2010. DOI: 10.1109/TBME.2010.2045123

Communications internationales avec actes et comité de lecture (International conferences)

  1. Chunfeng Lian, Hua Li, Pierre Vera, Su Ruan, “Unsupervised Co-Segmentation of Tumor in PET-CT Images Using Belief Functions Based Fusion”, IEEE-ISBI, Washington, US, April 2018.
  2. Jian Wu, Su Ruan, Chunfeng Lian, Mark Anastasio, Hua Li, “Heart Motion Tracking on Cine MRI Based on a Deep Boltzmann Machine-Driven Level Set Method”, IEEE-ISBI, Washington, US, April 2018.
  3. Jian Wu, Su Ruan, Hua Li, “Active Learning with Noise Modeling for Medical Image Annotation”, IEEE-ISBI, Washington, US, April 2018.
  4. Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen, “Medical Image Synthesis with Context-Aware Generative Adversarial Networks”, MICCAI 2017, Quebec, Canada, Sep. 10-14, 2017.
  5. Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan, « Fully automated esophagus segmentation with a hierarchical deep learning approach », ICSIPA 2017, Kuching Malaysia, Sep. 12-14, 2017.
  6. Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera, “Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images”, Int. Conf. IEEE-ICIP, Beijing China, September 2017.
  7. Roger Trullo, Caroline Petitjean, Su Ruan, Bernard Dubray, Dong Nie, Dinggang Shen, “Segmentation of Organs at Risk in Thoracic CT images using a SharpMask Architecture and Conditional Random Fields”, Int. Conf. IEEE- ISBI’2017, Melbourne Australia,  April 2017.
  8. Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera, “Tumor Delineation in FDG-PET Images Using A New Evidential Clustering Algorithm with Spatial Regularization And Adaptive Distance Metric”, Int. Conf. IEEE- ISBI’2017, Melbourne Australia,  April 2017.
  9. Kevin Gosse, Stephanie Jehan Besson, François Lecellier, Su Ruan, “Comparison of 2D and 3D Region-based Deformable Models and Random Walker Methods for PET Segmentation”, Int. Conf. IPTA’2016, Oulu, Finland, Dec. 2016.
  10. Chunfeng Lian, Hua Li, Thierry Denoeux, Pierre Vera, Su Ruan, “Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images”. MICCAI-2016, Athens, Greece, Oct. 2016.
  11. Jérôme Lapuyade-Lahorgue, Su Ruan, Hua Li, Pierre Vera, “Tumor segmentation by fusion of MRI images using copula based statistical methods”, IEEE-ICIP, Phoenix, USA, September 2016.
  12. Maxime Guinin, Su Ruan, Bernard Dubray, Laurent Massoptier, Isabelle Gardin, “Feature selection and patch-based segmentation in MRI for prostate radiotherapy”, IEEE-ICIP, Phoenix, USA, September 2016.
  13. Chunfeng Lian, Su Ruan, Thierry Denoeux, “Joint Feature Transformation and Selection Based on Dempster-Shafer Theory”. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) pp. 253-261, Belgium, June 2016.
  14. Chunfeng Lian, Hua Li, Thierry Denoeux, Pierre Vera, Su Ruan, « Dempster-Shafer Theory based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy », MICCAI-2015 , Munich, Germany, Octobre 2015.
  15. Saïd Ettaïeb, Kamel Hamrouni, Su Ruan, “Modelling and Tracking of Deformable Structures in Medical Images”, Int. Conf on Image and Graphics, Lecture Notes in Computer Science Volume 9218, 2015, pp 475-490, TianJin, China, Aug 2015.
  16. Chunfeng Lian, Hua Li, Thierry Denoeux, Hsin-Chen Chen, Clifford Robinson, Pierre Vera, Su Ruan, « Cancer Therapy Outcome Prediction based on Dempster-Shafer Theory and PET Imaging « , AAPM meeting 2015, Anaheim California, July 2015. ( accepted  as a finalist for the John R. Cameron young investigator competition of AAPM meeting 2015). 
  17. H.C. Chen · S. Dolly · J.R. Victoria · B.W. Fischer-Valuck · H. Wooten · R. Kashani · O.L. Green · S. Ruan · D. Low M.A. Anastasio · H. Li · V.L. Rodriguez · I. Kawrakow · R. Nana · J.F. Dempsey · S. Mutic · H.A. Gay · W.L. Thorstad, “An Anatomy Driven Contour Tracking Method to Quantify Pharyngeal Airway Motion Using On-board Cine MRI in Head and Neck Radiation Therapy”, International journal of radiation oncology, biology, physics 93(3):S21-S22, · November 2015.
  18. Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera, “Outcome prediction in tumour therapy based on dempster-shafer Theory”, IEEE-ISBI, Brooklyn, April 2015.
  19. Paul Desbordes, Romain Modzelewski, Su Ruan, Isabelle Gardin, Pierre Vera, “Prognostic and predictive values of initial 18FDG PET features using random forest classifier: Application to patients after chemo-radiotherapy for oesophageal cancer”, EANM’15 – Annual Congress of the European Association of Nuclear Medicine, October 10 – 14, in Hamburg/Germany, 2015.
  20. S Ruan, H Mi, C Petitjean, H Li, HC Chen, CG Robinson, B Dubray, P Vera, « Robust Optimal Feature Selection for Lung Tumor Recurrence Prediction in PET Imaging “, International Journal of Radiation Oncology• Biology• Physics, Vol 93(3), PP.S6,  Nov. 2015.
  21. H Chen, S Dolly, J Victoria, S Ruan, D Low, M Anastasio, B Fischer-Valuck, R Kashani, O Green, V Rodriguez, J Dempsey, S Mutic, H Gay, W Thorstad, H Li, “Assessment of Intra-/Inter-Fractional Internal Tumor and Organ Movement in Radiotherapy of Head and Neck Cancer Using On-Board Cine MRI”, Medical physics,Vol.42(6), pp. 3205-3206,  June, 2015.
  22. DesbordesR. Modzelewski, S. Ruan, S. Vauclin, P. Vera, I. Gardin, “Selection of Prognostic and Predictive Features on FDG PET Images Using Random Forest”, MICAAI workshop: Computational Methods for Molecular Imaging, Munich, Germany, October 2015.
  23.  Su Ruan,Hongmei Mi, Caroline Petitjean, Hua Li, Hsin-Chen Chen, Clifford Robinson, Bernard Dubray, Pierre Vera , “Robust Optimal Feature Selection for Lung Tumor Recurrence Prediction in PET Imaging”, Annual Meeting Scientific Program Committee of the American Society for Radiation Oncology (ASTRO), October 18-21 in San Antonio, US, 2015.
  24. Maxime Guinin, Su Ruan, Lamyaa Nkhali, Bernard Dubray, Laurent Massoptier and Isabelle Gardin, “Segmentation of Pelvic Organs at Risk Using Superpixels and Graph Diffusion in Prostate Radiotherapy”, IEEE-ISBI, Brooklyn, April 2015.
  25. Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan,  » Brain tumor segmentation from multiple MRI sequences using multiple kernel learning », IEEE-ICIP, Paris, October 2014.
  26. Paul Desbordes, Caroline Petitjean and Su Ruan, « 3D automated lymphoma segmentation in PET images based on cellular automata », IPTA-2014, Paris, September 2014.
  27. Hongmei Mi, Caroline Petitjean, Pierre Vera, Su Ruan, « Robust Feature Selection to Predict Tumor Treatmen Outcome »,   Computational Methods for Molecular Imaging, MICAAI workshop,  Boston, September 2014.
  28. Said Ettaieb, Kamel Hamrouni, Su Ruan, “Myocardium segmentation using a priori knowledge of shape and a spatial relation”, 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco, April 2014.
  29. Yu Guo, Su Ruan, Paul Walker, Yuanming Feng, « Prostate Cancer Segmentation from Multiparametric MRI Based on Fuzzy Bayesian Model » , IEEE-ISBI, Beijing, April 2014.
  30. D. Grosgeorge, C. Petitjean, S. Ruan, « Joint Segmentation of Right and Left Cardiac Ventricles Using Multi-Label Graph Cut », IEEE-ISBI, Beijing, April 2014.
  31. Hongmei Mi, Caroline Petitjean, Pierre Vera, Bernard Dubray, Su Ruan, « Automatic Lung Tumor Segmentation on PET Images Based on Random Walks and Tumor Growth Model », IEEE-ISBI, Beijing, April 2014.
  32. Ettaïeb, S., Hamrouni, K., Ruan, S., “Statistical Models of Shape and spatial relation-application to hippocampus segmentation”, VISAPP 2014 – Proceedings of the 9th International Conference on Computer Vision Theory and Applications, Volume 1, 2014, Pages 448-455.
  33. Hongmei Mi, Caroline Petitjean, Su Ruan, Pierre Vera, Bernard Dubray, « Predicting lung tumor evolution during radiotherapy from PET images using a patient specific model », IEEE-ISBI, San Francisco, April 2013.
  34. Benoît Lelandais, Isabelle Gardin,, Laurent Mouchard, Pierre Vera and Su Ruan, « Segmentation of Biological Target Volumes on Multi-tracer PET Images Based on Information Fusion for Achieving Dose Painting in Radiotherapy »,  MICCAI’2012, pp.545-549, Nice, France, Sept. 2012.
  35. D. Grosgeorge, C. Petitjean, S. Ruan, J. Caudron, et J. Dacher, « Right ventriclesegmentation by graph cut with shape prior », In3D Cardiovascular Imaging : a MICCAI segmentation challenge. France, 2012.
  36. Y. Guo, S. Ruan, J. Landré, Y. Zhang1, X. Ming1 and Y. Feng, « Localization of prostate cancer based on fuzzy fusion of multispectral MRI », World Congress on Medical Physics and Biomedical Engineering, pp. 1844-1846, Beijing, May 2012.
  37. Onoma D. P., Ruan S., Isabelle G., Monnehan G. A., Modzelewski R., Vera P., « 3D random walk based segmentation for lung tumor delineation in pet imaging », IEEE-ISBI, Barcelona, May 2012.
  38. P. D Onoma,S  Ruan . G. A. Monnehan, S. Tureau, R. Modzelewski, P. Vera, G Isabelle, « 3D Random walk based segmentation for delineation of heterogeneous positive tissues in PET imaging », In Annual Meeting of the Society of Nuclear Medicine and Molecular Imaging. États-Unis, 2012.
  39. P. D Onoma,S  Ruan . G. A. Monnehan, S. Tureau, R. Modzelewski, P. Vera, et I. Gardin. 3D Random Walk based Segmentation to Delineate heterogeneous BTV on 18FDG-PET images. In International Conference on Molecular Imaging in Radiation Oncology (MIRO). Autriche, 2012.
  40. Lelandais B., Gardin I., Mouchard L., Vera P., Ruan S., « Using belief function theory to deal with uncertainties and imprecisions in image processing », The 2nd International Conference on Belief Functions, Compiegne, May, 2012.
  41. S. Ruan, N. Zhang, Q. Liao and Y. Zhu, « Image fusion for following-up brain tumor evolution », IEEE-ISBI, Chicago, USA, April 2011.
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