Publications

 

Major publications since 2010 (the year I started working at the

University of Rouen Normandy)

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

2025

  1. 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
  2. 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.

2024

  1.  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
  2. 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”, Wiley, Medical Physics, July 2024. DOI: https://doi.org/10.1002/mp.17320
  3. 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
  4. 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.
  5. Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, »3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes », IEEE-ISBI, Athens, Greece, May 2024. arXiv preprint arXiv:2406.05421, DOI: 10.1109/ISBI56570.2024.10635533
  6. Thibaud Brochet, Kangfu Han, Jiale Cheng, Fenqiang Zhao, Jerome Lapuyade-Lahorgue, Su Ruan, Yi-Fang Tu , Sheng-Che Hung , and Gang Li, « Zneonatal Hypoxic Ischemic Encephalopathy Severity Grading Using Multimodal Swin Transformer », IEEE-ISBI, Athens, Greece, May 2024. DOI: 10.1109/ISBI56570.2024.10635742
  7. Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera and Su Ruan, « Deform-Mamba Network for MRI Super-Resolution »,  27th-MICCAI-2024, Marrakesh, Morocco, October, 2024. arXiv preprint arXiv:2407.05969
  8. Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan, “Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution”, The International Conference on Pattern Recognition (ICPR), Kolkata, India, December 2024. arXiv preprint  http://arXiv:2407.05993
  9. Léo Mottay, Hugo Hamon, Pierre Decazes, Sébastien Hapdey, and Su Ruan, “Neural Ordinary Differential Equations for Dynamic Dual-Tracer PET Image Separation in Silico”, MICAD, Manchester, UK, November,  2024.

2023

  1. Ling Huang, Su Ruan, Thierry Denœux, “Application of belief functions to medical image segmentation: A review”, Elsevier, Information Fusion, Volume 91, March 2023, Pages 737-756. https://doi.org/10.1016/j.inffus.2022.11.008, arXiv preprint arXiv:2205.01733
  2. Tongxue Zhou, Su Ruan, Haigen Hu, “A literature survey of MR-based brain tumor segmentation with missing Modalities”, Elsevier, Computerized Medical Imaging and Graphics, Volume 104, March 2023, 102167. https://doi.org/10.1016/j.compmedimag.2022.102167
  3. Ling Huang, Su Ruan, Thierry Denœux, « Semi-Supervised Multiple Evidence Fusion for Brain Tumor Segmentation », Elsevier, Neurocomputing, Volume 535, 28 Pages 40-52, May 2023. https://doi.org/10.1016/j.neucom.2023.02.047.   
  4. Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi
    Ghazouani, Sébastien Thureau, Maxime Fontanilles, Su Ruan, « Prediction of Brain Tumor Recurrence Location Based on Multi-modal Fusion and Nonlinear Correlation Learning », Elsevier, Computerized Medical Imaging and Graphics,  Volume 106, June 2023, 102218. https://doi.org/10.1016/j.compmedimag.2023.102218
  5. Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan, « Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review ». MDPI Journal of Imaging. 2023; 9(4):81. https://doi.org/10.3390/jimaging9040081
  6. Lixin Zhang, Yulun Sun, Pierre Decazes, Su Ruan, Yu Guo, Hui Yu, “One-Shot Learning for DLBCL Segmentation in Whole Body PET/CT Images”, IEEE-ISBI, Cartagena de Indias, Colombia, April 2023.
  7. Fethi Ghazouani, Pierre Vera, Su Ruan, « Efficient Brain Tumor Segmentation using Swin Transformer and Enhanced Local Self-Attention », CARS 2023, Munich Germany, June 2023.
  8. Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, « End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks »,  MICAD, Cambridge, UK, Dec.  2023.

2022

  1. Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu,  “A Tri-attention Fusion Guided Multi-modal Segmentation Network ”,  Elsevier, Pattern Recognition, Volume 124, 108417, April 2022. doi: https://doi.org/10.1016/j.patcog.2021.108417          http://arxiv.org/abs/2111.01623
  2. Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan, “Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images”, Elsevier, Pattern Recognition, Volume 124, 108452,  April 2022. doi: https://doi.org/10.1016/j.patcog.2021.108452
  3. Jérôme Lapuyade-Lahorgue, Su Ruan, « Segmentation of multicorrelated images with copula models and conditionally random fields« , SPIE, Journal of Medical Imaging, 9(1), 014001, 2022. https://doi.org/10.1117/1.JMI.9.1.014001
  4. Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, « A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction », MDPI, Entropy, 24(4), 436, 2022.  https://doi.org/10.3390/e24040436.
  5. Tongxue Zhou, Pierre Vera, Stéphane Canu, Su Ruan, “Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation”, Elsevier, Pattern Recognition Letters, Volume 158, June 2022, Pages 125-132, 2022, https://doi.org/10.1016/j.patrec.2022.04.019
  6. A Amyar, R Modzelewski, P Vera, V Morard, S Ruan, “Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction”, MDPI, Journal of Imaging, 8 (5), 130, 2022. https://doi.org/10.3390/jimaging8050130.   This pape has been selected as the journal issue cover in J. Imaging, Volume 8, Issue 5 (May 2022) : https://www.mdpi.com/2313-433X/8/5
  7. Ling Huang, Su Ruan, Pierre Decazes, Thierry Denœux, « Lymphoma segmentation from 3D PET-CT images using a deep evidential network », Elsevier, International Journal of Approximate Reasoning, Volume 149, Pages 39-60, October 2022. https://doi.org/10.1016/j.ijar.2022.06.007
  8. Zhengshan Huang, Yu Guo, Ning Zhang, XianHuang, Pierre Decazes, Stephanie Becker, Su Ruan, “Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images”, Elsevier, Computers in Biology and Medicine, Volume 151, Part A, 106230, December  2022. https://doi.org/10.1016/j.compbiomed.2022.106230
  9. Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan, “Multi-task multi-scale learning for outcome prediction in 3D PET images”, Elsevier, Computers in Biology and Medicine, Volume 151, Part A, 106208, December  2022. https://doi.org/10.1016/j.compbiomed.2022.106208
  10. Maliazurina Saad, Shenghua He, Wade Thorstad, Hiram Gay, Daniel Barnett, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li “Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers”, IEEE Transactions on Radiation and Plasma Medical Sciences,Volume: 6, Issue: 2, February 2022. 10.1109/TRPMS.2021.3104297
  11. Tongxue Zhou, Alexandra Noeuveglise, Fethi Ghazouani, Romain Modzelewski, Sébastien Thureau, Maxime Fontanilles, Su Ruan, “Prediction of brain tumor recurrence location based on Kullback–Leibler divergence and nonlinear correlation learning”, 26th International Conference on Pattern Recognition (ICPR), Canada, August, 2022.
  12. Ling Huang, Thierry Denoeux, Pierre, Vera, Su Ruan, « Evidence fusion with contextual discounting for multi-modality medical image segmentation », 25th MICCAI-2022, Singapore, September, 2022. https://arxiv.org/pdf/2206.11739.pdf
  13. Jannane Nada, Jérôme Lapuyade-Lahorgue, Fethi Ghazouani, Sébastien Bougleux, Su Ruan, « MR image synthesis using Riemannian geometry constrained in VAE », 16th IEEE ICSP, China, Octobre 2022.
  14. Abdelouahad Achmamad, Fethi Ghazouani, Su Ruan, « Few shot learning for brain tumor segmentation », 16th IEEE ICSP, China, Octobre 2022.
  15. Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera and Su Ruan, « Deep Learning Based Radiomics To Predict Treatment Response Using Multi-Datasets », MICAD2022, UK, November, 2022.

2021

  1. Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan, “Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities”,  IEEE Trans.on Image Processing, 2021. Vol. 30, pp:4263 – 4274 . https://arxiv.org/abs/2104.06231 DOI: 10.1109/TIP.2021.3070752
  2. Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang and Hua Li, “A novel machine learning approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers”, Oxford Academic, Bioinformatics, Volume 37, Issue 19, Pages 3106–3114,  October 2021.  https://doi.org/10.1093/bioinformatics/btab242
  3. Tongxue Zhou, Stéphane Canu, Su Ruan, “Automatic COVID‐19 CT Segmentation Using U‐Net Integrated Spatial and Channel Attention Mechanism”, Wiley, Int. Journal of Imaging Systems and Technology. Volume31, Issue1 Pages 16-27, March 2021. https://doi.org/10.1002/ima.22527
  4. Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan, “Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities”, Elsevier, Neurocomputing, Volume 466,  Pages 102-112, 27 November 2021. https://doi.org/10.1016/j.neucom.2021.09.032
  5. S Ruan, « Advanced Computational Intelligence in Medical and Biomedical Imaging »,
    Elsevier, IRBM 42 (6), 399, 2021. https://doi.org/10.1016/j.irbm.2021.09.001
  6. T. Brochet, J. Lapuyade-Lahorgue, S. Bougleux, M. Salaün, S. Ruan, »Deep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy », Elsevier, IRBM, Volume 42, Issue 6, Pages 400-406, 2021, https://doi.org/10.1016/j.irbm.2021.06.006.
  7. Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan, “3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constrain », 25th. Int. Conf. ICPR, Milan, Italy, January 2021. https://arxiv.org/pdf/2102.03111.pdf
  8. Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li, “Deep learning-based multi-class COVID-19 classification with x-ray Images”, SPIE Medical Imaging, San Diego, United States, February 2021. https://doi.org/10.1117/12.2582261
  9. Tongxue Zhou, Stéphane Canu,  Pierre Vera, and Su Ruan, “A Dual Supervision Guided Attentional Network for Multimodal MR brain Tumor Segmentation”, International Conferenc on Medical Image and Computer-Aided Diagnosis, Birmingham, UK, March. 2021. https://link.springer.com/chapter/10.1007/978-981-16-3880-0_1
  10. Ling Huang, Su Ruan, Thierry Denoeux, « Belief function-based semi-supervised learning for brain tumor segmentation », IEEE- ISBI, Nice, April 2021. https://arxiv.org/pdf/2102.00097.pdf
  11. Ling Huang, Su Ruan, Thierry Denoeux, « Covid-19 classification with deep neural network and belief functions », The Fifth International Conference on Biological Information and Biomedical Engineering (BIBE2021), July 2021, Hangzhou China. https://arxiv.org/ftp/arxiv/papers/2101/2101.06958.pdf
  12. Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan, “Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation”, MICCAI- MLMI, Strasbourg, Oct. 2021. https://arxiv.org/pdf/2108.05422.pdf
  13. Ling Huang, Thierry Denoeux, Pierre Decazes, Su Ruan, “Evidential segmentation of 3D PET/CT images”, BELIEF 2021: 6th International Conference on Belief Functions, October 15-19, 2021, Shanghai, China. https://arxiv.org/pdf/2104.13293.pdf

2020

  1. Tongxue Zhou, Stéphane Canu, Su Ruan, “Fusion based on attention mechanism and context constrain for multi-modal brain tumor segmentation”, Elsevier, Computerized Medical Imaging and Graphics, Volume 86, 101811. December 2020. https://doi.org/10.1016/j.compmedimag.2020.101811
  2. Amine Amyar, Romain Modzelewski,, Hua Li, Su Ruan,   “ Multi-task Deep Learning Based CT Imaging Analysis For COVID-19 Pneumonia: Classification and Segmentation”, Elsevier, Computers in Biology and Medicine, Volume 126, 104037. November 2020. https://doi.org/10.1016/j.compbiomed.2020.104037
  3.  Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan,  » Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian », Elsevier, Digital Signal Processing, Volume 101, June 2020. https://doi.org/10.1016/j.dsp.2020.102703
  4. Tongxue Zhou,  Stephane Canu, Pierre Vera, Su Ruan,  « Brain tumor segmentation with missing modalities via latent multi-source correlation representation »,  Int. Conf.  MICCAI, Lima, Peru, October 2020. https://arxiv.org/pdf/2003.08870.pdf
  5. Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski, « RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images », International Conference on Biomedical Engineering and Bioinformatics, Berlin, September 2020. https://arxiv.org/pdf/2003.08663.pdf
  6. Yu Guo, Pierre Decazes, Stéphanie Becker, Hua Li, Su Ruan, « Deep disentangled representation learning of pet images for lymphoma outcome prediction « , Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https://ieeexplore.ieee.org/abstract/document/9098477 
  7. Haigen Hu, Leizhao Shen, Tongxue Zhou, Pierre Decazes, Pierre Vera, Su Ruan, « Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy « , Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https://ieeexplore.ieee.org/abstract/document/9098595
  8. Tongxue Zhou, Su Ruan, Yu Guo, Stephane Canu, « A multi-modality fusion network based on attention mechanism for brain tumor segmentation »,   Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https://ieeexplore.ieee.org/abstract/document/9098392

2019

  1. T. Zhou, S. Ruan, Sté. Canu,  “A review: Deep learning for medical image segmentation using multi-modality fusion ”, Elsevier, ARRAY, volumes 3–4, September–December 2019.    doi: https://doi.org/10.1016/j.array.2019.100004
  2. Haigen Hu, Pierre Decazes, Pierre Vera, Hua Li, Su Ruan, “Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy”, Springer, International Journal of Computer Assisted Radiology and Surgery, Volume 14, Issue 10, pp 1715-1724, October 2019.   https://doi.org/10.1007/s11548-019-02049-2
  3. Yuan Liu, Stephane Canu, Paul Honeine, Su Ruan, “Mixed Integer Programming for Sparse Coding: Application to Image Denoising”, IEEE Transactions on Computational Imaging,Volume: 5, Issue: 3, Page(s): 354 – 365, Sept 2019.      DOI: 10.1109/TCI.2019.2896790.
  4. A. Amyar ; S. Ruan ; I. Gardin ; C. Chatelain ; P. Decazes ; R. Modzelewski, “3D RPET-NET: Development of a 3D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction”, IEEE Transactions on Radiation and Plasma Medical Sciences, Volume: 3 Issue: 2, page : 225 – 231, March 2019. DOI: 10.1109/TRPMS.2019.2896399
  5. Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, Pierre Vera, “Joint Tumor Segmentation in PET-CT Images using Co-Clustering and Fusion based on Belief Functions”, IEEE Transactions on Image Processing, Volume: 28 , Issue: 2 , Feb. page: 755 – 766, Feb. 2019. DOI: 10.1109/TIP.2018.2872908
  6. Roger Trullo, Caroline Petitjean, Bernard Dubray, Su Ruan, “Multiorgan segmentation using distance-aware adversarial networks”, SPIE, Journal of Medical Imaging, 6(1), 014001 10, January 2019. DOI: https://doi.org/10.1117/1.JMI.6.1.014001
  7. Jian Wu, Chunfeng Lian, Su Ruan , Thomas R. Mazur , Sasa Mutic , Mark A. Anastasio , Perry W. Grigsby, Pierre Vera ,  Hua Li, “Treatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory”,   IEEE Transactions on Radiation and Plasma Medical Sciences, Volume: 3, Issue: 2, page : 216 – 224, March 2019. DOI: 10.1109/TRPMS.2018.2872406
  8. Fan Wang, Chunfeng Lian, Pierre Vera, Su Ruan, “Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG–PET images”, Springer, Multimedia Systems,   Vol.25(2): 127-133. Avril 2019. DOI: https://doi.org/10.1007/s00530-017-0579-0
  9. Tongxue Zhou, Su Ruan, Haigen Hu, Stéphane Canu, “Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI”. MLMI@MICCAI 2019: 574-582, Shenzhen, China, Oct. 2019. https://link.springer.com/chapter/10.1007/978-3-030-32692-0_66
  10. Haigen Hu, Chao Du, Pierre Decazes, Pierre Vera, Su Ruan, “A Prior Knowledge Integrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, USA, Nov. 2019. https://ieeexplore.ieee.org/abstract/document/8983082
  11. Haigen Hu, Chao Du, Qiu Guan, Qianwei Zhou, Pierre Vera, Su Ruan, “A Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images”, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, USA, Nov. 2019. https://ieeexplore.ieee.org/abstract/document/8983179
  12. Haigen Hu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan, “Gaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images”, 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China, Nov. 2019. https://ieeexplore.ieee.org/abstract/document/8965932
  13. Haigen Hu, Pierre Decazes , Pierre Vera , Hua Li , Su Ruan, “Detection and Segmentation of Lymphomas in 3D PET Images via Clustering with Entropy based
    Optimization Strategy”, Conf. Int. CARS, Rennes France, 2019.

2018

  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, Volume: 65, Issue:12, page: 2720 – 2730,  Dec. 2018. Doi:10.1109/TBME.2018.2814538
  2. Yuntao Yu, Pierre Decaze, Jérôme Lapuyade- Lahorgue, Isabelle Gardin, Pierre Vera,  Su Ruan, “Semi-automatic lymphoma detection and segmentation using fully conditional random fields”, Elsevier, Computerized Medical Imaging and Graphics, Vol.70, Pages 1-7, December 2018.    https://doi.org/10.1016/j.compmedimag.2018.09.001
  3. 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.
  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. 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.
  6. 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.
  7. Jian Wu, Su Ruan, Hua Li, “Active Learning with Noise Modeling for Medical Image Annotation”, IEEE-ISBI, Washington, US, April 2018.

2017

  1. 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
  2. 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/10.101/j.compmedimag.2016.12.002
  3. 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
  4. 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 »,  Quarterly Journal of Nuclear Medicine and Molecular Imaging, Vol 61 (3), pp: 301-313, September, 2017. DOI: 10.23736/S1824-4785.17.02756-X
  5. 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.
  6. Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan, « Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures », MICCAI workshop: Deep Learn Med Image Anal & Multimodal Learn Clin Decis Support. Quebec, Canada, Sep. 2017. https://link.springer.com/chapter/10.1007/978-3-319-67558-9_3
  7. 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.
  8. 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.
  9. 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.
  10. 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.

2016

  1. 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
  2. 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.
  3. Damien Grosgeorge, Caroline Petitjean, and Su Ruan, « A multilabel statistical shape prior for image segmentation », IET Image Processing,  10(10):710-716, Oct. 2016. doi: 10.1049/iet-ipr.2015.0408
  4. 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
  5. 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
  6. 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.
  7. 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, Octobre 2016.
  8. 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.
  9. 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.
  10. 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.

2015

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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.
  6. 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.
  7. 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). 
  8. 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.
  9. Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera, “Outcome prediction in tumour therapy based on dempster-shafer Theory”, IEEE-ISBI, Brooklyn, April 2015.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14.  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.
  15. 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.

2014

  1. Pierre Buyssens, Isabelle Gardin, 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
  2. 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,Vol.38(8), pp. 753–763, December 2014.  doi:10.1016/j.compmedimag.2014.09.007
  3. 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
  4. 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.2014.2301892
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan, « Brain tumor segmentation from multiple MRI sequences using multiple kernel learning », IEEE-ICIP, Paris, October 2014.
  11. Paul Desbordes, Caroline Petitjean and Su Ruan, « 3D automated lymphoma segmentation in PET images based on cellular automata », IPTA-2014, Paris, September 2014.
  12. 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.
  13. 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.
  14. Yu Guo, Su Ruan, Paul Walker, Yuanming Feng, « Prostate Cancer Segmentation from Multiparametric MRI Based on Fuzzy Bayesian Model « , IEEE-ISBI, Beijing, April 2014.
  15. D. Grosgeorge, C. Petitjean, S. Ruan, « Joint Segmentation of Right and Left Cardiac Ventricles Using Multi-Label Graph Cut », IEEE-ISBI, Beijing, April 2014.
  16. 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.
  17. 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.

2013

  1. 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 
  2. 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
  3. 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
  4. 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
  5. 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.
  6. Yu Guo, Su Ruan, “Signal Separation with A Priori Knowledge Using Sparse Representation”, Book chapter, In Amitava Chatterjee, Hadi Nobahari,and Patrick Siarry, editors, Advances in Heuristic Signal Processing and Applications, pp 315-332, Springer, 2013.

2012

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

2011

  1. 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
  2. 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
  3. S. Ruan, N. Zhang, Q. Liao and Y. Zhu, « Image fusion for following-up brain tumor evolution », IEEE-ISBI, Chicago, USA, April 2011.

2010

  1. 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
  2. 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