Pr. Su Ruan

Pr. Su Ruan

Fonction : Professeur des Universités

Groupe : Quantif

Biographie

Su RUAN
Full Professor at the University of Rouen Normandy, France

Education and Diplomas
• Jan. 1993 Ph.D. , University of Rennes I, France, Jan. 1993
(Thèse de doctorat, Laboratoire de Traitement du Signal et de l’Image (INSERM-LTSI), soutenue à l’Université de Rennes I)
• Dec. 2000 Professorship Diploma HDR, University of Caen, Dec. 2000.
(Habilitation à Diriger des Recherches (HDR) , Labo. GREYC UMR 6072 CNRS, soutenue à l’Université de Caen.)

Professional Activities
• 1992 – 1993 Assistant professor at the National Institute for Applied Sciences of Rennes, France
(ATER à l’INSA de Rennes)
• 1993 – 2003 Associate professor at the University of Caen, France
(Maître de conférences à l’Université de Caen)
• 2003 – 2010 Full professor at the University of Reims Champagne-Ardenne, France
(Professeure des universités à l’IUT de Troyes de l’Université de Reims Champagne-Ardenne)
• Since 2010 Full professor at the University of Rouen Normandy, France
(Professeure des universités à l’Université de Rouen Normandie)
• 2010-2024 Co-leader of the team QUANTIF
(Co-responsable de l’équipe Quantif)
• 2014-2021 Co-Animator of the « Analysis and image processing » group of Normastic research federation (CNRS FR n°3638)
(Co-Animatrice de l’axe « Analyse et traitement d’images » de la fédération de recherche Normastic, CNRS FR n°3638)
• 2015-2023 Deputy Scientific Director of GDR-ISIS (Directeur scientifique adjoint du GDR-ISIS)
• 2019-2025 Member of National Council of Universities (Membre de la section CNU 61) : https://www.conseil-national-des-universites.fr/cnu/#/ 
• Since 2025 Member of the National Committee for Scientific Research ( Membre du Comité national de la recherche scientifique) : https://www.cnrs.fr/comitenational/sections/section_acc.htm
• Since 2023 Board member of GRETSI (Groupe de Recherche et d’Etudes de Traitement du Signal et des Images) (Membre CA du GRETSI) : https://www.gretsi.fr/membres
• Since 2017 Board member of SFGBM (Société Française de Génie Biologique et Médical) (Membre CA de la SFGMB ) : https://sfgbm.fr/
https://sfgbm.fr/archives/10907#more-10907
https://sfgbm.fr/archives/11202
https://www.sfgbm.fr/archives/11353

• Associate Editor or Area Editor for 3 Elsevier journals
Computerized Medical Imaging and Graphic:
https://www.sciencedirect.com/journal/computerized-medical-imaging-and-graphics
Array : https://www.sciencedirect.com/journal/array
IRBM : https://www.journals.elsevier.com/irbm
• HECKTOR challenge at MICCAI 2022
https://hecktor.grand-challenge.org/Overview

Research Areas
(Thèmes de recherche développés)
Machine learning & Deep learning, theory of belief functions for
a) Image segmentation et classification
b) Information fusion
c) Outcome prediction
d) Applications in medical imaging (IRM; PET/CT)

Teaching (Enseignement) :
Master Ingénierie de la Santé, Ingénierie pour le Bio-médical- IBIOM (Master of Health Engineering: Bio-medical Engineering)
• Fondation and Manager of the IBIOM M1 and M2 master’s program (Porteur et Responsable du parcours master IBIOM M1 et M2)
https://formation.univ-rouen.fr/fr/catalogue-de-l-offre-de-formation/master-lmd-XB/master-ingenierie-de-la-sante-L5CKUPEO/master-ingenierie-de-la-sante-ingenierie-pour-le-bio-medical-L5CKUQEX.html

Publications récentes :
List of publications: https://scholar.google.fr/citations?user=mjB2a6MAAAAJ&hl=fr

1.Tongxue Zhou, Ph.D. Su Ruan Baiying Lei, “BUFNet: Boundary-aware and Uncertainty-driven Multi-modal Fusion Network for MR Brain Tumor Segmentation”, Medical Image Analysis, Volume 107, Part B, 103855, January 2026.

2.Ling Huang, Yucheng Xing, Qika Lin, Jinming Duan, Su Ruan, Mengling Feng, “EsurvFusion: An evidential multimodal survival fusion model based on Epistemic random fuzzy sets”, IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2025.3623879, 2026.

3.Xiaoyan Kui, Zexin Ji, Beiji Zou, Yang Li, Yulan Dai, Liming Chen, Pierre Vera, Su Ruan, “Iterative Collaboration Network Guided By Reconstruction Prior for Medical Image Super-Resolution”, IEEE Transactions on Computational Imaging, vol. 11, pp. 827-838, 2025, doi: 10.1109/TCI.2025.3577340.

4.Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, “Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation”, Elsevier, Computerized Medical Imaging and Graphics, Volume 123, 102532, 2025.

5.Ling Huang, Su Ruan, Pierre Decazes, Thierry Denœux, “Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation”, Elsevier, Information Fusion. Volume 113, 102648, January 2025. DOI: https://doi.org/10.1016/j.inffus.2024.102648

6.Zexin Ji, Beiji Zou, Xiaoyan Kui, Hua Li, Pierre Vera, Su Ruan “Generation of Super-Resolution for Medical Image via a Self-prior Guided Mamba Network with Edge-aware Constraint”, Elsevier Pattern Recognition letters, Volume 187, Pages 93-99, January 2025. https://doi.org/10.1016/j.patrec.2024.11.020.

7.Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, “Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes”, Elsevier, Neurocomputing, Volume 606, 14 November 2024, 128360. DOI: https://doi.org/10.1016/j.neucom.2024.128360. arXiv preprint arXiv:2406.11659.

8.Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng, “A review of uncertainty quantification in medical image analysis: probabilistic and nonprobabilistic methods”, Elsevier Medical Image Analysis, Volume 97, October 2024, 103223. DOI: 10.1016/j.media.2024.103223

9.Zong Fan, Xiaohui Zhang, Su Ruan, Wade Thorstad, Hiram Gay, Pengfei Song, Xiaowei Wang, Hua Li, “A medical image classification method based on self-regularized adversarial learning”, Medical Physics, July 2024. DOI: https://doi.org/10.1002/mp.17320

10.F. Ghazouani, P. Vera, S. Ruan, « Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention», Springer International Journal of Computer Assisted Radiology and Surgery, Volume 19, pages 273–281, 2024. https://doi.org/10.1007/s11548-023-03024-8

Publications



231 documents

  • Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera. Accurate Tumor Segmentation In FDG-PET Images With Guidance Of Complementary CT Images. IEEE International Conference on Image Processing (ICIP 2017), Sep 2017, Beijing, China. pp.4447-4451, ⟨10.1109/ICIP.2017.8297123⟩. ⟨hal-02553135⟩
  • Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan. Fully automated esophagus segmentation with a hierarchical deep learning approach. 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Sep 2017, Kuching, Malaysia. pp.503-506. ⟨hal-02113939⟩
  • Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, et al.. Medical Image Synthesis with Context-Aware Generative Adversarial Networks. Medical image computing and computer-assisted intervention : MICCAI .. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017, 10435, pp.417-425. ⟨10.1007/978-3-319-66179-7_48⟩. ⟨hal-02054406⟩
  • Nie Dong, Roger Trullo, Lian Jun, Caroline Petitjean, Su Ruan, et al.. Medical image synthesis with context-aware generative adversarial networks. Miccai, Sep 2017, Quebec, Canada. ⟨hal-01676320⟩
  • Koutoua Anouan, Benoît Lelandais, Agathe Edet-Sanson, Su Ruan, Pierre Vera, et al.. 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 : official publication of the Italian Association of Nuclear Medicine (AIMN) [and] the International Association of Radiopharmacology (IAR), [and] Section of the Society of Radiopharmaceutic, 2017, pp.301-313. ⟨10.23736/S1824-4785.17.02756-X⟩. ⟨hal-01648101⟩
  • Jérôme 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, 2017, 26 (7), pp.3187 - 3195. ⟨10.1109/TIP.2017.2685345⟩. ⟨hal-01637152⟩
  • Paul Desbordes, Su Ruan, Romain Modzelewski, Sébastien Vauclin, Pierre Vera, et al.. 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. Annual meeting of the Society of Nuclear Medicine and Molecular Imaging, Jun 2017, Denver, United States. ⟨hal-01649722⟩
  • 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. 14th IEEE International Symposium on Biomedical Imaging (ISBI 2017), Apr 2017, Melbourne, Australia. pp.1177-1180, ⟨10.1109/ISBI.2017.7950726⟩. ⟨hal-02553198⟩
  • Roger Trullo, Caroline Petitjean, Su Ruan, Dong Nie, Dinggang Shen, et al.. SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS. IEEE Internation Symposium on Biomedical Imaging, Apr 2017, Melbourne, Australia. pp.1003-1006, ⟨10.1109/ISBI.2017.7950685⟩. ⟨hal-01650973⟩
  • Paul Desbordes, Su Ruan, Romain Modzelewski, Pascal Pineau, Sébastien Vauclin, et al.. 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, 2017, 12 (3), pp.e0173208. ⟨10.1371/journal.pone.0173208⟩. ⟨hal-01637198⟩