{"id":7,"date":"2016-01-19T09:24:19","date_gmt":"2016-01-19T08:24:19","guid":{"rendered":"http:\/\/pagesperso.litislab.fr\/suruan\/?page_id=7"},"modified":"2024-11-29T08:10:27","modified_gmt":"2024-11-29T07:10:27","slug":"publications","status":"publish","type":"page","link":"https:\/\/pagesperso.litislab.fr\/suruan\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

\u00a0<\/h3>\r\n

M<\/strong>ajor publications since 2010 (the year I started working at the<\/span><\/strong><\/span><\/h3>\r\n

University of <\/strong><\/span>Rouen Normandy<\/span><\/strong><\/span>)<\/span><\/strong><\/span><\/h3>\r\n

https:\/\/scholar.google.fr\/citations?user=mjB2a6MAAAAJ&hl=fr<\/a><\/p>\r\n

2025<\/em><\/strong><\/p>\r\n

    \r\n
  1. Ling Huang, Su Ruan, Pierre Decazes, Thierry Den\u0153ux, \u201cDeep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation\u201d, Elsevier, Information Fusion<\/strong>.\u00a0 Volume 113<\/span><\/span><\/a>, 102648, January 2025. DOI: https:\/\/doi.org\/10.1016\/j.inffus.2024.102648<\/a><\/span><\/li>\r\n
  2. Zexin Ji, Beiji Zou, Xiaoyan Kui, Hua Li, Pierre Vera, Su Ruan \u201cGeneration of Super-Resolution for Medical Image via a Self-prior Guided Mamba Network with Edge-aware Constraint\u201d, Elsevier Pattern Recognition Letters<\/strong>, Volume 187<\/span><\/span><\/a>, Pages 93-99, January 2025. https:\/\/doi.org\/10.1016\/j.patrec.2024.11.020<\/a><\/span>.<\/span><\/li>\r\n<\/ol>\r\n

    2024<\/em><\/strong><\/p>\r\n

      \r\n
    1. \u00a0F. Ghazouani, P. Vera,\u00a0 <\/span>S. Ruan, \u201cEfficient brain tumor segmentation using Swin transformer and enhanced local self-attention\u201d,\u00a0 Springer International Journal of Computer Assisted Radiology and Surgery<\/strong>, <\/span>Volume\u00a019<\/span><\/span>, pages 273\u2013281, 2024. https:\/\/doi.org\/10.1007\/s11548-023-03024-8<\/a><\/span><\/li>\r\n
    2. Zong Fan, Xiaohui Zhang, Su Ruan, Wade Thorstad, Hiram Gay, Pengfei Song, Xiaowei Wang, Hua Li, \u201cA medical image classification method based on self-regularized adversarial learning\u201d, Wiley, Medical Physics<\/strong>, July 2024. DOI: <\/span>https:\/\/doi.org\/10.1002\/mp.17320<\/a><\/li>\r\n
    3. Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng, \u201cA review of uncertainty quantification in medical image analysis: probabilistic and nonprobabilistic methods\u201d, Elsevier Medical Image Analysis,<\/strong> Volume 97<\/span><\/a>, October 2024, 103223. DOI: 10.1016\/j.media.2024.103223<\/a><\/span><\/li>\r\n
    4. Aghiles Kebaili, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera, Su Ruan, \u201cDiscriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes\u201d, Elsevier, Neurocomputing<\/strong>, Volume 606<\/span><\/span><\/a>, 14 November 2024, 128360. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2024.128360<\/a>. arXiv preprint arXiv:2406.11659<\/a>.<\/span><\/span>
      <\/li>\r\n
    5. Aghiles Kebaili, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera, Su Ruan,\u00a0\u00bb3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes\u00a0\u00bb, IEEE-ISBI, Athens, Greece, May 2024. arXiv preprint arXiv:2406.05421, DOI:\u00a0<\/strong><\/a>10.1109\/ISBI56570.2024.10635533<\/a><\/span><\/li>\r\n
    6. Thibaud Brochet, Kangfu Han, Jiale Cheng, Fenqiang Zhao, Jerome Lapuyade-Lahorgue, Su Ruan, Yi-Fang Tu , Sheng-Che Hung , and Gang Li, \u00ab\u00a0Zneonatal Hypoxic Ischemic Encephalopathy Severity Grading Using Multimodal Swin Transformer\u00a0\u00bb, IEEE-ISBI, Athens, Greece, May 2024. DOI:\u00a0<\/strong>10.1109\/ISBI56570.2024.10635742<\/a><\/span><\/li>\r\n
    7. Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera and Su Ruan, \u00ab\u00a0Deform-Mamba Network for MRI Super-Resolution\u00a0\u00bb,\u00a0 27th-MICCAI-2024, Marrakesh, Morocco, October, 2024. arXiv preprint arXiv:2407.05969<\/a><\/span><\/li>\r\n
    8. Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan, \u201cSelf-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution\u201d, The International Conference on Pattern Recognition (ICPR), Kolkata, India, December 2024. arXiv preprint \u00a0http:\/\/arXiv:2407.05993<\/a><\/span><\/li>\r\n
    9. L\u00e9o Mottay, Hugo Hamon, Pierre Decazes, S\u00e9bastien Hapdey, and Su Ruan, \u201cNeural Ordinary Differential Equations for Dynamic Dual-Tracer PET Image Separation in Silico\u201d, MICAD, Manchester, UK, November,\u00a0 2024.<\/span><\/li>\r\n<\/ol>\r\n

      2023<\/strong><\/p>\r\n

        \r\n
      1. Ling Huang, Su Ruan, Thierry Den\u0153ux, \u201cApplication of belief functions to medical image segmentation: A review\u201d, Elsevier, Information Fusion<\/strong>, Volume 91<\/a>,\u00a0March 2023, Pages 737-756. https:\/\/doi.org\/10.1016\/j.inffus.2022.11.008<\/a>, arXiv preprint arXiv:2205.01733<\/span><\/li>\r\n
      2. Tongxue Zhou, Su Ruan, Haigen Hu, \u201cA literature survey of MR-based brain tumor segmentation with missing Modalities\u201d, Elsevier, Computerized Medical Imaging and Graphics<\/strong>, Volume 104, March 2023, 102167. https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102167<\/a><\/span><\/li>\r\n
      3. Ling Huang, Su Ruan, Thierry Den\u0153ux, \u00ab\u00a0Semi-Supervised Multiple Evidence Fusion for Brain Tumor Segmentation\u00a0\u00bb, Elsevier, Neurocomputing<\/strong>, Volume 535<\/span><\/a>, 28 Pages 40-52, May 2023. https:\/\/doi.org\/10.1016\/j.neucom.2023.02.047<\/a>.\u00a0 \u00a0<\/span><\/li>\r\n
      4. Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi<\/span>
        Ghazouani, S\u00e9bastien Thureau, Maxime Fontanilles, Su Ruan, \u00ab\u00a0Prediction of Brain Tumor Recurrence Location Based on Multi-modal Fusion and Nonlinear Correlation Learning\u00a0\u00bb, Elsevier, Computerized Medical Imaging and Graphics<\/strong>,\u00a0         Volume 106<\/span><\/a>,\u00a0June 2023, 102218. https:\/\/doi.org\/10.1016\/j.compmedimag.2023.102218<\/a><\/span><\/li>\r\n
      5. Aghiles Kebaili, J\u00e9r\u00f4me Lapuyade-Lahorgue, Su Ruan, \u00ab\u00a0Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review\u00a0\u00bb. MDPI Journal of Imaging<\/strong>. 2023; 9(4):81. https:\/\/doi.org\/10.3390\/jimaging9040081<\/a><\/span>
        <\/li>\r\n
      6. Lixin Zhang, Yulun Sun, Pierre Decazes, Su Ruan, Yu Guo, Hui Yu, \u201cOne-Shot Learning for DLBCL Segmentation in Whole Body PET\/CT Images\u201d, IEEE-ISBI, Cartagena de Indias, Colombia, April 2023.<\/span><\/li>\r\n
      7. Fethi Ghazouani, Pierre Vera, Su Ruan, \u00ab\u00a0Efficient Brain Tumor Segmentation using Swin Transformer and Enhanced Local Self-Attention\u00a0\u00bb, CARS 2023, Munich Germany, June 2023.<\/span><\/li>\r\n
      8. Aghiles Kebaili, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera, Su Ruan, \u00ab\u00a0<\/i>End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks\u00a0\u00bb,\u00a0 MICAD, Cambridge, UK, Dec.\u00a0 2023.<\/span><\/li>\r\n<\/ol>\r\n

        2022<\/em><\/strong><\/span><\/p>\r\n

          \r\n
        1. Tongxue Zhou, Su Ruan, Pierre Vera, St\u00e9phane Canu,\u00a0 \u201cA Tri-attention Fusion Guided Multi-modal Segmentation Network \u201d,\u00a0 Elsevier, Pattern Recognition<\/strong>, Volume 124, 108417, April 2022. doi: https:\/\/doi.org\/10.1016\/j.patcog.2021.108417 <\/a>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0http:\/\/arxiv.org\/abs\/2111.01623<\/a><\/span><\/li>\r\n
        2. Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan, \u201cDeep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images\u201d, Elsevier, Pattern Recognition,<\/strong> Volume 124<\/a>, 108452,\u00a0 April 2022. doi: https:\/\/doi.org\/10.1016\/j.patcog.2021.108452<\/a><\/span><\/li>\r\n
        3. J\u00e9r\u00f4me Lapuyade-Lahorgue, Su Ruan, \u00ab\u00a0Segmentation of multicorrelated images with copula models and conditionally random fields<\/span>\u00ab\u00a0, SPIE, Journal of Me<\/strong>dical Imaging,<\/strong> 9(1), 014001, 2022. https:\/\/doi.org\/10.1117\/1.JMI.9.1.014001<\/a><\/span><\/li>\r\n
        4. Thibaud Brochet, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera, Su Ruan, \u00ab\u00a0A Quantitative Comparison between Shannon and Tsallis\u2013Havrda\u2013Charvat Entropies Applied to Cancer Outcome Prediction\u00a0\u00bb, MDPI, Entropy<\/strong>, 24(4), 436, 2022.\u00a0 https:\/\/doi.org\/10.3390\/e24040436<\/a>.<\/span><\/li>\r\n
        5. Tongxue Zhou, Pierre Vera, St\u00e9phane Canu, Su Ruan, \u201cMissing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation\u201d, Elsevier, Pattern Recognition Letters<\/strong>, Volume 158<\/a>,\u00a0June 2022, Pages 125-132, 2022, https:\/\/doi.org\/10.1016\/j.patrec.2022.04.019<\/a><\/span><\/li>\r\n
        6. A Amyar, R Modzelewski, P Vera, V Morard, S Ruan, \u201cWeakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction\u201d, MDPI,\u00a0Journal of Imaging, <\/strong>8 (5), 130, 2022. https:\/\/doi.org\/10.3390\/jimaging8050130. <\/a>\u00a0 This pape has been selected as the journal issue cover in J. Imaging<\/em>, Volume 8, Issue 5 (May 2022)<\/em>\u00a0:<\/span> https:\/\/www.mdpi.com\/2313-433X\/8\/5<\/a><\/li>\r\n
        7. Ling Huang, Su Ruan, Pierre Decazes, Thierry Den\u0153ux, \u00ab\u00a0Lymphoma segmentation from 3D PET-CT images using a deep evidential network\u00a0\u00bb, Elsevier, International Journal of Approximate Reasoning<\/strong>, Volume 149<\/a>, Pages 39-60, October 2022. https:\/\/doi.org\/10.1016\/j.ijar.2022.06.007<\/a><\/span><\/li>\r\n
        8. Zhengshan Huang, Yu Guo, Ning Zhang, XianHuang, Pierre Decazes, Stephanie Becker, Su Ruan, \u201cMulti-scale feature similarity-based weakly supervised lymphoma segmentation in PET\/CT images\u201d, Elsevier, Computers in Biology and Medicine<\/strong>, Volume 151, Part A, 106230, December \u00a02022.<\/span> https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106230 <\/a><\/li>\r\n
        9. Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan, \u201cMulti-task multi-scale learning for outcome prediction in 3D PET images\u201d, Elsevier,<\/span> Computers in Biology and Medicine<\/strong>, Volume 151, Part A, 106208, December \u00a02022. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106208<\/a><\/li>\r\n
        10. Maliazurina Saad, Shenghua He, Wade Thorstad, Hiram Gay, Daniel Barnett, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li \u201cLearning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers\u201d, IEEE Transactions on Radiation and Plasma Medical Sciences<\/strong>,Volume: 6, Issue: 2, February 2022. 10.1109\/TRPMS.2021.3104297<\/a><\/span><\/li>\r\n
        11. Tongxue Zhou, Alexandra Noeuveglise, Fethi Ghazouani, Romain Modzelewski, S\u00e9bastien Thureau, Maxime Fontanilles, Su Ruan, \u201cPrediction of brain tumor recurrence location based on Kullback\u2013Leibler divergence and nonlinear correlation learning\u201d, 26th International Conference on Pattern Recognition (ICPR), Canada, August, 2022.<\/span><\/li>\r\n
        12. Ling Huang, Thierry Denoeux, Pierre, Vera, Su Ruan, \u00ab\u00a0Evidence fusion with contextual discounting for multi-modality medical image segmentation\u00a0\u00bb, 25th MICCAI-2022,<\/span> Singapore, September, 2022. https:\/\/arxiv.org\/pdf\/2206.11739.pdf<\/a><\/li>\r\n
        13. Jannane Nada, J\u00e9r\u00f4me Lapuyade-Lahorgue, Fethi Ghazouani, S\u00e9bastien Bougleux, Su Ruan, \u00ab\u00a0MR image synthesis using Riemannian geometry constrained in VAE\u00a0\u00bb, 16th IEEE ICSP, China, Octobre 2022.<\/span><\/li>\r\n
        14. Abdelouahad Achmamad, Fethi Ghazouani, Su Ruan, \u00ab\u00a0Few shot learning for brain tumor segmentation\u00a0\u00bb, 16th IEEE ICSP, China, Octobre 2022.<\/span><\/li>\r\n
        15. Thibaud Brochet, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera and Su Ruan, \u00ab\u00a0Deep Learning Based Radiomics To Predict Treatment Response Using Multi-Datasets\u00a0\u00bb, MICAD2022, <\/a>UK, November, 2022.<\/span><\/li>\r\n<\/ol>\r\n

          2021<\/em><\/strong><\/p>\r\n

            \r\n
          1. Tongxue Zhou, St\u00e9phane Canu, Pierre Vera, Su Ruan, \u201cLatent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities\u201d,\u00a0 IEEE Trans.on Image Processing<\/strong>, 2021. Vol. 30, pp:4263 – 4274 . https:\/\/arxiv.org\/abs\/2104.06231<\/a> DOI<\/span>:\u00a010.1109\/TIP.2021.3070752<\/a><\/li>\r\n
          2. Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang and Hua Li, \u201cA novel machine learning approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers\u201d, Oxford Academic, Bioinformatics<\/strong>, Volume 37, Issue 19, Pages 3106\u20133114, \u00a0October 2021. \u00a0https:\/\/doi.org\/10.1093\/bioinformatics\/btab242<\/a><\/span><\/li>\r\n
          3. Tongxue Zhou, St\u00e9phane Canu, Su Ruan, \u201cAutomatic COVID\u201019 CT Segmentation Using U\u2010Net Integrated Spatial and Channel Attention Mechanism\u201d, Wiley<\/span>, Int. Journal of Imaging Systems and Technology<\/strong>. Volume31,\u00a0Issue1 Pages 16-27, March 2021. https:\/\/doi.org\/10.1002\/ima.22527<\/a><\/li>\r\n
          4. Tongxue Zhou, St\u00e9phane Canu, Pierre Vera, Su Ruan, \u201cFeature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities\u201d, Elsevier, Neurocomputing<\/strong>, Volume 466,\u00a0 Pages 102-112, 27 November 2021.<\/span> https:\/\/doi.org\/10.1016\/j.neucom.2021.09.032<\/a><\/li>\r\n
          5. S Ruan, \u00ab\u00a0Advanced Computational Intelligence in Medical and Biomedical Imaging\u00a0\u00bb,\r\n
            Elsevier, <\/span>IRBM<\/strong> 42 (6), 399, 2021. https:\/\/doi.org\/10.1016\/j.irbm.2021.09.001<\/a><\/div>\r\n<\/li>\r\n
          6. T. Brochet, J. Lapuyade-Lahorgue, S. Bougleux, M. Sala\u00fcn, S. Ruan,\u00a0\u00bbDeep Learning Using Havrda-Charvat Entropy for Classification of Pulmonary Optical Endomicroscopy\u00a0\u00bb, Elsevier, <\/span>IRBM<\/strong>, Volume 42, Issue 6, Pages 400-406, 2021, https:\/\/doi.org\/10.1016\/j.irbm.2021.06.006<\/a>.<\/li>\r\n
          7. Tongxue Zhou, St\u00e9phane Canu, Pierre Vera, Su Ruan, \u201c3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constrain\u00a0\u00bb, 25th. Int. Conf. ICPR, Milan, Italy, January 2021<\/span>. https:\/\/arxiv.org\/pdf\/2102.03111.pdf<\/span><\/li>\r\n
          8. Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li, \u201cDeep learning-based multi-class COVID-19 classification with x-ray Images\u201d, SPIE Medical Imaging, San Diego, United States, February 2021<\/span>. https:\/\/doi.org\/10.1117\/12.2582261<\/span><\/li>\r\n
          9. Tongxue Zhou, St\u00e9phane Canu,\u00a0 Pierre Vera, and Su Ruan, \u201cA Dual Supervision Guided Attentional Network for Multimodal MR brain Tumor Segmentation\u201d, 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<\/a><\/span><\/li>\r\n
          10. Ling Huang, Su Ruan, Thierry Denoeux, \u00ab\u00a0Belief function-based semi-supervised learning for brain tumor segmentation\u00a0\u00bb, IEEE- ISBI, Nice, April 2021.<\/span> https:\/\/arxiv.org\/pdf\/2102.00097.pdf<\/span><\/li>\r\n
          11. Ling Huang, Su Ruan, Thierry Denoeux, \u00ab\u00a0Covid-19 classification with deep neural network and belief functions\u00a0\u00bb, The Fifth International Conference on Biological Information and Biomedical Engineering (BIBE2021), July 2021, Hangzhou China.<\/span> https:\/\/arxiv.org\/ftp\/arxiv\/papers\/2101\/2101.06958.pdf<\/span><\/li>\r\n
          12. Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan, \u201cDeep PET\/CT fusion with Dempster-Shafer theory for lymphoma segmentation\u201d, MICCAI- MLMI, Strasbourg, Oct. 2021. https:\/\/arxiv.org\/pdf\/2108.05422.pdf<\/a><\/span><\/li>\r\n
          13. Ling Huang, Thierry Denoeux, Pierre Decazes, Su Ruan, \u201cEvidential segmentation of 3D PET\/CT images\u201d, BELIEF 2021: 6th International Conference on Belief Functions, October 15-19, 2021, Shanghai, China. https:\/\/arxiv.org\/pdf\/2104.13293.pdf<\/a><\/span><\/li>\r\n<\/ol>\r\n

            2020<\/em><\/strong><\/p>\r\n

              \r\n
            1. Tongxue Zhou, St\u00e9phane Canu, Su Ruan, \u201cFusion based on attention mechanism and context constrain for multi-modal brain tumor segmentation\u201d, Elsevier, Computerized Medical Imaging and Graphics<\/strong>, Volume 86<\/a>, 101811. December 2020.\u00a0<\/span>https:\/\/doi.org\/10.1016\/j.compmedimag.2020.101811<\/a><\/li>\r\n
            2. Amine Amyar, Romain Modzelewski,, Hua Li, Su Ruan, \u00a0\u00a0\u201c\u00a0Multi-task Deep Learning Based CT Imaging Analysis For COVID-19 Pneumonia: Classification and Segmentation\u201d, Elsevier, Computers in Biology and Medicine<\/strong>, Volume 126<\/a>,\u00a0104037. November 2020. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.104037<\/a><\/span><\/li>\r\n
            3. \u00a0Yuan Liu, St\u00e9phane Canu, Paul Honeine, Su Ruan,\u00a0\u00a0\u00bb\u00a0Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian\u00a0\u00bb, Elsevier, Digital Signal Processing<\/strong>, Volume 101<\/a>,\u00a0June 2020. https:\/\/doi.org\/10.1016\/j.dsp.2020.102703<\/a><\/span><\/li>\r\n
            4. Tongxue Zhou,\u00a0 Stephane Canu, Pierre Vera, Su Ruan,\u00a0\u00a0\u00ab\u00a0Brain tumor segmentation with missing modalities via latent multi-source correlation representation\u00a0\u00bb, \u00a0Int. Conf.\u00a0 MICCAI, Lima, Peru, October 2020. https:\/\/arxiv.org\/pdf\/2003.08870.pdf<\/span><\/span><\/li>\r\n
            5. Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski, \u00ab\u00a0RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images\u00a0\u00bb, International Conference on Biomedical Engineering and Bioinformatics, Berlin, September 2020. https:\/\/arxiv.org\/pdf\/2003.08663.pdf<\/span><\/span><\/li>\r\n
            6. Yu Guo, Pierre Decazes, St\u00e9phanie Becker, Hua Li, Su Ruan, \u00ab\u00a0Deep disentangled representation learning of pet images for lymphoma outcome prediction\u00a0\u00ab\u00a0, Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9098477<\/span>\u00a0<\/span><\/li>\r\n
            7. Haigen Hu, Leizhao Shen, Tongxue Zhou, Pierre Decazes, Pierre Vera, Su Ruan, \u00ab\u00a0Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy \u00ab\u00a0, Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9098595<\/span><\/span><\/li>\r\n
            8. Tongxue Zhou, Su Ruan, Yu Guo, Stephane Canu, \u00ab\u00a0A multi-modality fusion network based on attention mechanism for brain tumor segmentation\u00a0\u00bb, \u00a0\u00a0Int. Conf. IEEE-ISBI, Iowa, USA, April 2020. https:\/\/ieeexplore.ieee.org\/abstract\/document\/9098392<\/span><\/span><\/li>\r\n<\/ol>\r\n

              2019<\/em><\/strong><\/span><\/p>\r\n

                \r\n
              1. T. Zhou, S. Ruan, St\u00e9. Canu, \u00a0\u201cA review: Deep learning for medical image segmentation using multi-modality fusion \u201d, Elsevier, ARRAY,\u00a0<\/strong>volumes 3\u20134,\u00a0September\u2013December 2019. \u00a0 <\/strong>\u00a0doi: https:\/\/doi.org\/10.1016\/j.array.2019.100004<\/a><\/span><\/li>\r\n
              2. Haigen Hu, Pierre Decazes, Pierre Vera, Hua Li, Su Ruan, \u201cDetection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy\u201d, Springer, International Journal of Computer Assisted Radiology and Surgery<\/strong>, Volume 14, Issue\u00a010<\/a>, pp 1715-1724,\u00a0October\u00a02019.\u00a0 \u00a0https:\/\/doi.org\/10.1007\/s11548-019-02049-2<\/a><\/span><\/li>\r\n
              3. Yuan Liu, Stephane Canu, Paul Honeine, Su Ruan, \u201cMixed Integer Programming for Sparse Coding: Application to Image Denoising\u201d, IEEE Transactions on Computational Imaging<\/strong>,Volume: 5, Issue: 3<\/a>, Page(s): <\/strong>354 – 365, Sept 2019.\u00a0 \u00a0 \u00a0\u00a0DOI: <\/strong>10.1109\/TCI.2019.2896790<\/a>.<\/span><\/li>\r\n
              4. A. Amyar ; S. Ruan ; I. Gardin ; C. Chatelain ; P. Decazes ; R. Modzelewski, \u201c3D RPET-NET: Development of a 3D PET Imaging Convolutional Neural Network for Radiomics Analysis and Outcome Prediction\u201d,\u00a0IEEE Transactions on Radiation and Plasma Medical Sciences,<\/strong> Volume: 3 Issue: 2, page : 225 – 231, March 2019. <\/span><\/span>DOI:\u00a0<\/strong>10.1109\/TRPMS.2019.2896399<\/a><\/span><\/li>\r\n
              5. Chunfeng Lian, Su Ruan, Thierry Den\u0153ux, Hua Li, Pierre Vera, \u201cJoint Tumor Segmentation in PET-CT Images using Co-Clustering and Fusion based on Belief Functions\u201d, IEEE Transactions on Image Processing<\/strong>,\u00a0Volume: 28\u00a0,\u00a0Issue: 2<\/a>\u00a0, Feb. page:\u00a0755\u00a0– 766, Feb. 2019.\u00a0DOI:\u00a0<\/strong>10.1109\/TIP.2018.2872908<\/a><\/span><\/li>\r\n
              6. Roger Trullo, Caroline Petitjean, Bernard Dubray, Su Ruan, \u201cMultiorgan segmentation using distance-aware adversarial networks\u201d, SPIE, Journal of Medical Imaging<\/strong>, 6(1)<\/a>, 014001 10, January 2019.\u00a0DOI:<\/strong> https:\/\/doi.org\/10.1117\/1.JMI.6.1.014001<\/a><\/span><\/li>\r\n
              7. Jian Wu,\u00a0Chunfeng Lian,\u00a0Su Ruan\u00a0,\u00a0Thomas R. Mazur\u00a0,\u00a0Sasa Mutic\u00a0,\u00a0Mark A. Anastasio\u00a0,\u00a0Perry W. Grigsby,\u00a0Pierre Vera\u00a0,\u00a0\u00a0Hua Li,\u00a0\u201cTreatment Outcome Prediction for Cancer Patients based on Radiomics and Belief Function Theory\u201d, \u00a0 IEEE Transactions on Radiation and Plasma Medical Sciences,<\/strong> Volume: 3, Issue: 2, page\u00a0: 216\u00a0– 224, March 2019.\u00a0DOI:\u00a010.1109\/TRPMS.2018.2872406<\/a><\/span><\/li>\r\n
              8. Fan Wang, Chunfeng Lian, Pierre Vera, Su Ruan, \u201cAdaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG\u2013PET images\u201d, Springer,\u00a0Multimedia Systems<\/strong>,\u00a0\u00a0 Vol.25(2)<\/a>: 127-133. Avril 2019. DOI: https:\/\/doi.org\/10.1007\/s00530-017-0579-0<\/span><\/span><\/li>\r\n
              9. Tongxue Zhou, Su Ruan, Haigen Hu, St\u00e9phane Canu, \u201cDeep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI\u201d.\u00a0MLMI@MICCAI\u00a02019<\/a>: 574-582, Shenzhen, China, Oct. 2019. https:\/\/link.springer.com\/chapter\/10.1007\/978-3-030-32692-0_66<\/span><\/span><\/li>\r\n
              10. Haigen Hu, Chao Du, Pierre Decazes, Pierre Vera, Su Ruan, \u201cA Prior Knowledge Integrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs\u201d, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, USA, Nov. 2019. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8983082<\/span><\/span><\/li>\r\n
              11. Haigen Hu, Chao Du, Qiu Guan, Qianwei Zhou, Pierre Vera, Su Ruan, \u201cA Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images\u201d, IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, USA, Nov. 2019. https:\/\/ieeexplore.ieee.org\/abstract\/document\/8983179<\/span><\/span><\/li>\r\n
              12. Haigen Hu, Pierre Decazes, J\u00e9r\u00f4me Lapuyade-Lahorgue, Pierre Vera, Su Ruan, \u201cGaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images\u201d, 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<\/span><\/span><\/li>\r\n
              13. Haigen Hu, Pierre Decazes , Pierre Vera , Hua Li , Su Ruan,\u00a0\u201cDetection and Segmentation of Lymphomas in 3D PET Images via Clustering with Entropy based<\/span>
                Optimization Strategy\u201d, Conf. Int. CARS, Rennes France, 2019.<\/span><\/li>\r\n<\/ol>\r\n

                2018<\/em><\/strong><\/span><\/p>\r\n

                  \r\n
                1. Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang,\u00a0Dinggang Shen, \u201cMedical Image Synthesis with Deep Convolutional Adversarial Networks\u201d,\u00a0IEEE Transactions on Biomedical Engineering<\/strong>, Volume: 65, Issue:12, page: 2720 \u2013 2730, \u00a0Dec. 2018. Doi:10.1109\/TBME.2018.2814538<\/a><\/span><\/li>\r\n
                2. Yuntao Yu, Pierre Decaze, J\u00e9r\u00f4me Lapuyade- Lahorgue, Isabelle Gardin, Pierre Vera,\u00a0 Su Ruan, \u201cSemi-automatic lymphoma detection and segmentation using fully conditional random fields\u201d, Elsevier, Computerized Medical Imaging and Graphics,<\/strong> Vol.70, Pages 1-7, December 2018.\u00a0 \u00a0 https:\/\/doi.org\/10.1016\/j.compmedimag.2018.09.001<\/a><\/span><\/li>\r\n
                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, \u201cA Deep Boltzmann Machine-Driven Level Set Method for Heart Motion Tracking Using Cine MRI\u201d,\u00a0Elsevier, Medical Image Analysis<\/strong>, Volume 47, Pages\u00a068\u201380, July, 2018.\u00a0 DOI: https:\/\/doi.org\/10.1016\/j.media.2018.03.015<\/a>.<\/span><\/li>\r\n
                4. Chunfeng Lian, Su Ruan, Thierry Den\u0153ux, Hua Li, Pierre Vera, \u201cSpatial Evidential Clustering with Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images\u201d, IEEE. Trans. on Biomedical Engineering<\/strong>, Volume: 65, Issue: 1<\/a>, pp. 21 – 30, Jan. 2018. DOI: <\/strong>10.1109\/TBME.2017.2688453<\/a><\/span><\/li>\r\n
                5. Chunfeng Lian, Hua Li, Pierre Vera, Su Ruan, \u201cUnsupervised Co-Segmentation of Tumor in PET-CT Images Using Belief Functions Based Fusion\u201d, IEEE-ISBI, Washington, US, April 2018.<\/span><\/li>\r\n
                6. Jian Wu, Su Ruan, Chunfeng Lian, Mark Anastasio, Hua Li, \u201cHeart Motion Tracking on Cine MRI Based on a Deep Boltzmann Machine-Driven Level Set Method\u201d, IEEE-ISBI, Washington, US, April 2018.<\/span><\/li>\r\n
                7. Jian Wu, Su Ruan, Hua Li, \u201cActive Learning with Noise Modeling for Medical Image Annotation\u201d, IEEE-ISBI, Washington, US, April 2018.<\/span><\/li>\r\n<\/ol>\r\n

                  2017<\/em><\/strong><\/span><\/p>\r\n

                    \r\n
                  1. J\u00e9rome Lapuyade-Lahorgue, Jing-Hao Xue, Su Ruan, \u201c Segmenting Multi-Source images using hidden Markov fields with copula-based multivariate statistical distributions\u201d, IEEE Transactions on Image Processing<\/strong>. Volume: 26, Issue: 7, pp: 3187-3195, July 2017. \u00a0doi:\u00a0<\/strong>10.1109\/TIP.2017.2685345<\/a><\/span><\/li>\r\n
                  2. Desbordes Paul, Ruan Su, Modzelewski Romain, Vauclin S\u00e9bastien, Vera Pierre, Gardin Isabelle, \u201cFeature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier\u201d, Elsevier, Computerized Medical Imaging and Graphics, <\/strong>Volume 60<\/a><\/strong>,\u00a0\u00a0<\/strong>Pages 42-49, September 2017.\u00a0 http:\/\/dx.doi.org\/10.101\/j.compmedimag.2016.12.002<\/span><\/li>\r\n
                  3. Paul Desbordes, Su Ruan, Romain Modzelewski, Pascal Pineau, S\u00e9bastien Vauclin, Pierrick Gouel, Pierre Michel, Fr\u00e9d\u00e9ric Di Fiore, Pierre Vera, Isabelle Gardin, \u201cPredictive 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\u201d, PloS one<\/strong>, Vol.12(3), March 2017. http:\/\/dx.doi.org\/10.1371\/journal.pone.0173208<\/span><\/li>\r\n
                  4. Anouan K. J., Lelandais B., Edet-Sanson A., Ruan S., Vera P., Gardin I., Hapdey S, \u00ab\u00a018F-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\u00a0\u00bb,\u00a0\u00a0Quarterly Journal of Nuclear Medicine and Molecular Imaging<\/strong>, Vol 61 (3), pp: 301-313, September, 2017. DOI:\u00a010.23736\/S1824-4785.17.02756-X<\/a><\/span><\/li>\r\n
                  5. Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen, \u201cMedical Image Synthesis with Context-Aware Generative Adversarial Networks\u201d, MICCAI 2017, Quebec, Canada, Sep. 10-14, 2017.<\/span><\/li>\r\n
                  6. Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan, \u00ab\u00a0Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures\u00a0\u00bb, 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<\/span><\/span><\/li>\r\n
                  7. Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan, \u00ab\u00a0Fully automated esophagus segmentation with a hierarchical deep learning approach\u00a0\u00bb, ICSIPA 2017, Kuching Malaysia, Sep. 12-14, 2017.<\/span><\/li>\r\n
                  8. Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera, \u201cAccurate tumor segmentation in FDG-PET images with guidance of complementary CT images\u201d, Int. Conf. IEEE-ICIP, Beijing China, September 2017.<\/span><\/li>\r\n
                  9. Roger Trullo, Caroline Petitjean, Su Ruan, <\/em>Bernard Dubray, Dong Nie, Dinggang Shen, \u201cSegmentation of Organs at Risk in Thoracic CT images using a SharpMask Architecture and Conditional Random Fields\u201d, Int. Conf. IEEE- ISBI\u20192017, Melbourne Australia,\u00a0 April 2017.<\/span><\/li>\r\n
                  10. Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera, \u201cTumor Delineation in FDG-PET Images Using A New Evidential Clustering Algorithm with Spatial Regularization And Adaptive Distance Metric\u201d, Int. Conf. IEEE- ISBI\u20192017, Melbourne Australia,\u00a0 April 2017.<\/span><\/li>\r\n<\/ol>\r\n

                    2016<\/em><\/strong><\/span><\/p>\r\n

                      \r\n
                    1. Chunfeng Lian, Su Ruan, and Thierry Denoeux \u00ab\u00a0Dissimilarity Metric Learning in the Belief Function Framework\u00a0\u00bb, IEEE Transactions on Fuzzy Systems<\/strong>, Volume: 24, Issue: 6, pp. 1555 – 1564,\u00a0\u00a0Dec. 2016.\u00a0doi:10.1109\/TFUZZ.2016.2540068<\/a><\/span><\/li>\r\n
                    2. Chunfeng Lian,\u00a0<\/sup>Su Ruan,\u00a0Thierry Den\u0153ux, Fabrice Jardin,\u00a0Pierre Vera, \u00ab\u00a0Selecting Radiomic Features from FDG-PET Images for Cancer Treatment Outcome Prediction\u00a0\u00bb, Elsevier, Medical Image Analysis<\/strong>, Volume 32, Pages 257\u2013268, August 2016. doi:10.1016\/j.media.2016.05.007<\/a>.<\/span><\/li>\r\n
                    3. Damien Grosgeorge, Caroline Petitjean, and Su Ruan, \u00ab\u00a0A multilabel statistical shape prior for image segmentation\u00a0\u00bb, IET Image Processing<\/strong>,\u00a0 10(10):710-716, Oct.\u00a02016. doi: 10.1049\/iet-ipr.2015.0408<\/a><\/span><\/li>\r\n
                    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, \u201cAn integrated model-driven method for in-treatment upper airway motion tracking using cine MRI in head and neck radiation therapy\u201d, Medical Physics<\/strong>, Vol. 43 (8), pp. 4700-4710, August 2016. DOI\u00a0: 10.1118\/1.4955118<\/span><\/li>\r\n
                    5. Paul Desbordes, Caroline Petitjean, Su Ruan, \u00ab\u00a0Segmentation of lymphoma tumor in PET images using cellular automata: A preliminary study\u00a0\u00bb, Elsevier, IRBM<\/strong>, Volume 37, Issue 1<\/a>, Pages 3\u201310,<\/em> 2016. doi:10.1016\/j.irbm.2015.11.001<\/a><\/span><\/li>\r\n
                    6. Kevin Gosse, Stephanie Jehan Besson, Fran\u00e7ois Lecellier, Su Ruan,\u00a0\u201cComparison of 2D and 3D Region-based Deformable Models and Random Walker Methods for PET Segmentation\u201d, Int. Conf. IPTA\u20192016, Oulu, Finland, Dec. 2016.<\/span><\/li>\r\n
                    7. Chunfeng Lian, Hua Li, Thierry Denoeux,\u00a0Pierre Vera, Su Ruan, \u00ab\u00a0Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images\u00a0\u00bb. MICCAI-2016,\u00a0 Athens, Greece, Octobre 2016.<\/span><\/li>\r\n
                    8. J\u00e9r\u00f4me Lapuyade-Lahorgue, Su Ruan, Hua Li, Pierre Vera, \u201cTumor segmentation by fusion of MRI images using copula based statistical methods\u201d, IEEE-ICIP, Phoenix, USA, September 2016.<\/span><\/li>\r\n
                    9. Maxime Guinin, Su Ruan, Bernard Dubray, Laurent Massoptier, Isabelle Gardin, \u201cFeature selection and patch-based segmentation in MRI for prostate radiotherapy\u201d, IEEE-ICIP, Phoenix, USA, September 2016.<\/span><\/li>\r\n
                    10. Chunfeng Lian, Su Ruan, Thierry Denoeux, \u201cJoint Feature Transformation and Selection Based on Dempster-Shafer Theory\u201d. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) pp. 253-261, Belgium, June 2016.<\/span><\/li>\r\n<\/ol>\r\n

                      2015<\/em><\/strong><\/p>\r\n

                        \r\n
                      1. Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan, \u00ab\u00a0Robust Feature Selection to Predict Tumor Treatment Outcome\u00a0\u00bb, Elsevier, Artificial Intelligence in Medicine<\/strong>, Volume 64, Issue 3,\u00a0 Pages 195\u2013204, July 2015.\u00a0 doi:10.1016\/j.artmed.2015.07.002<\/a><\/span><\/li>\r\n
                      2. Hongmei Mi, Caroline Petitjean, Pierre Vera, Su Ruan, \u00ab\u00a0Joint Tumor Growth Prediction and Tumor Segmentation on Therapeutic Follow-up PET Images\u00a0\u00bb, Elsevier, Medical Image Analysis<\/strong>, Volume 23, Issue 1, Pages\u00a084\u201391, July 2015. doi:10.1016\/j.media.2015.04.016<\/a><\/span><\/li>\r\n
                      3. Chunfeng Lian, Su Ruan, Thierry Den\u0153ux, \u00ab\u00a0An evidential classifier based on feature selection and two-step classification strategy\u00a0\u00bb, Elsevier, Pattern Recognition<\/strong>, Volume 48, Issue 7, Pages 2318\u20132327, July 2015.\u00a0doi:10.1016\/j.patcog.2015.01.019<\/a><\/span><\/li>\r\n
                      4. Caroline Petitjean, Maria A. Zuluaga, Wenjia Bai, Jean-NicolasDacher, Damien Grosgeorge, J\u00e9r\u00f4me 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, S\u00e9bastien 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, \u00ab\u00a0Right Ventricle Segmentation From Cardiac MRI: A Collation Study\u00a0\u00bb, Elsevier, Medical Image Analysis<\/strong>, Volume 19, Issue 1, Pages 187\u2013202, January 2015.\u00a0 doi:10.1016\/j.media.2014.10.004<\/a><\/span><\/li>\r\n
                      5. Chunfeng Lian, Hua Li, Thierry Denoeux,\u00a0Pierre Vera, Su Ruan, \u00ab\u00a0Dempster-Shafer Theory based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy\u00a0\u00bb, MICCAI-2015 , Munich, Germany, Octobre 2015.<\/span><\/li>\r\n
                      6. Sa\u00efd Etta\u00efeb, Kamel Hamrouni, Su Ruan, \u201cModelling and Tracking of Deformable Structures in Medical Images\u201d, Int. Conf on Image and Graphics, Lecture Notes in Computer Science Volume 9218, 2015, pp 475-490, TianJin, China, Aug 2015.<\/span><\/li>\r\n
                      7. Chunfeng Lian, Hua Li, Thierry Denoeux, Hsin-Chen Chen, Clifford Robinson, Pierre Vera, Su Ruan, \u00ab\u00a0Cancer Therapy Outcome Prediction based on Dempster-Shafer Theory and PET Imaging\u00a0\u00bb, AAPM meeting 2015, Anaheim California, July 2015. (accepted\u00a0 as a finalist for the John R. Cameron young investigator competition of AAPM meeting 2015).\u00a0<\/span><\/li>\r\n
                      8. H.C. Chen \u00b7 S. Dolly \u00b7 J.R. Victoria \u00b7 B.W. Fischer-Valuck \u00b7 H. Wooten \u00b7 R. Kashani \u00b7 O.L. Green \u00b7 S. Ruan \u00b7 D. Low\u00a0M.A. Anastasio \u00b7 H. Li \u00b7 V.L. Rodriguez \u00b7 I. Kawrakow \u00b7 R. Nana \u00b7 J.F. Dempsey \u00b7 S. Mutic \u00b7 H.A. Gay \u00b7 W.L. Thorstad, \u201cAn Anatomy Driven Contour Tracking Method to Quantify Pharyngeal Airway Motion Using On-board Cine MRI in Head and Neck Radiation Therapy\u201d, International journal of radiation oncology, biology, physics 93(3):S21-S22, \u00b7 November 2015.<\/span><\/li>\r\n
                      9. Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera, \u201cOutcome prediction in tumour therapy based on dempster-shafer Theory\u201d, IEEE-ISBI, Brooklyn, April 2015.<\/span><\/li>\r\n
                      10. Paul Desbordes,\u00a0Romain Modzelewski, Su Ruan, Isabelle Gardin, Pierre Vera, \u201cPrognostic and predictive values of initial 18FDG PET features using random forest classifier: Application to patients after chemo-radiotherapy for oesophageal cancer\u201d, EANM’15 – Annual Congress of the European Association of Nuclear Medicine, October 10 – 14, in Hamburg\/Germany, 2015.<\/span><\/li>\r\n
                      11. S Ruan, H Mi, C Petitjean, H Li, HC Chen, CG Robinson, B Dubray, P Vera, \u00ab\u00a0Robust Optimal Feature Selection for Lung Tumor Recurrence Prediction in PET Imaging \u00ab\u00a0, International Journal of Radiation Oncology\u2022 Biology\u2022 Physics, Vol 93(3), PP.S6, \u00a0Nov. 2015.<\/span><\/li>\r\n
                      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, \u201cAssessment of Intra-\/Inter-Fractional Internal Tumor and Organ Movement in Radiotherapy of Head and Neck Cancer Using On-Board Cine MRI\u201d, Medical physics,Vol.42(6), pp. 3205-3206, \u00a0June, 2015.<\/span><\/li>\r\n
                      13. DesbordesR. Modzelewski, S. Ruan, S. Vauclin, P. Vera, I. Gardin, \u201cSelection of Prognostic and Predictive Features on FDG PET Images Using Random Forest\u201d, MICAAI workshop: Computational Methods for Molecular Imaging, Munich, Germany, October 2015.<\/span><\/li>\r\n
                      14. \u00a0Su Ruan, <\/u>Hongmei Mi, Caroline Petitjean, Hua Li, Hsin-Chen Chen, Clifford Robinson, Bernard Dubray, Pierre Vera , \u201cRobust Optimal Feature Selection for Lung Tumor Recurrence Prediction in PET Imaging\u201d, Annual Meeting Scientific Program Committee of the American Society for Radiation Oncology (ASTRO), October 18-21 in San Antonio, US, 2015.<\/span><\/li>\r\n
                      15. Maxime Guinin, Su Ruan, Lamyaa Nkhali, Bernard Dubray, Laurent Massoptier and Isabelle Gardin, \u201cSegmentation of Pelvic Organs at Risk Using Superpixels and Graph Diffusion in Prostate Radiotherapy\u201d, IEEE-ISBI, Brooklyn, April 2015.<\/span><\/li>\r\n<\/ol>\r\n

                        2014<\/em><\/strong><\/p>\r\n

                          \r\n
                        1. Pierre Buyssens, Isabelle Gardin, Su Ruan, Abderrahim Elmoataz, \u00ab\u00a0Eikonal-based region growing for efficient clustering\u00a0\u00bb, Elsevier, Image and Vision Computing<\/strong>,\u00a0 Vol. 32 (12),\u00a0 Pages 1045\u20131054, December 2014. \u00a0doi:10.1016\/j.imavis.2014.10.002<\/a><\/span><\/li>\r\n
                        2. D.P. Onoma, S. Ruan, S. Thureau, L. Nkhali, R. Modzelewski, G.A. Monnehan, P. Vera, I. Gardin, \u00ab\u00a0Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-Locally Adaptive Random Walk algorithm\u00a0\u00bb, Elsevier, Computerized Medical Imaging and Graphics<\/strong>,Vol.38(8), pp. 753\u2013763, December 2014. \u00a0doi:10.1016\/j.compmedimag.2014.09.007<\/a><\/span><\/li>\r\n
                        3. Benoit Lelandais, Su Ruan, Thierry Denoeux, Pierre Vera, Isabelle Gardin, \u00ab\u00a0Fusion of multi-tracer PET images for Dose Painting\u00a0\u00bb, Elsevier, Medical Image Analysis<\/strong>, Volume 18, Issue 7, Pages 1247\u20131259, October 2014. \u00a0\u00a0doi:10.1016\/j.media.2014.06.014<\/a><\/span><\/li>\r\n
                        4. Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan, \u00ab\u00a0Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients with PET\u00a0\u00bb,\u00a0 IEEE Transaction on Medical Imaging<\/strong>, Volume: 33 (4), pp: 995-1003, 2014. \u00a010.1109\/TMI.2014.2301892<\/a><\/span><\/li>\r\n
                        5. Laurent D. Cohen, Khalifa Djemal, Su Ruan, Christine Toumoulin, Special Issue on biomedical image segmentation using variational and statistical approaches, Elsevier,<\/span> IRBM<\/strong>, Volume 35 (1), pp: 1-2, February, 2014. \u00a0doi:10.1016\/j.irbm.2014.02.001<\/a><\/li>\r\n
                        6. Pierre\u00a0 Buyssens,\u00a0 Isabelle Gardin, Su. Ruan, \u00ab\u00a0Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in CT images\u00a0\u00bb, Elsevier, IRBM<\/strong>, Volume 35 (1), pp:20-26, 2014. \u00a0doi:10.1016\/j.irbm.2013.12.007<\/a><\/span><\/li>\r\n
                        7. B. Dubray, S. Thureau, L. Nkhali, R. Modzelewski, K. Doyeux, S. Ruan, P. Vera, \u00ab\u00a0FDG-PET imaging for radiotherapy target volume definition in lung cancer\u00a0\u00bb, Elsevier, IRBM<\/strong>, Volume 35 (1),\u00a0 pp:41-45, 2014.\u00a0doi:10.1016\/j.irbm.2013.12.008<\/a><\/span><\/li>\r\n
                        8. Benoit Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan, \u00ab\u00a0Dealing with uncertainty and imprecision in image segmentation using belief function theory\u00a0\u00bb, Elsevier, International Journal of Approximate Reasoning<\/strong>, Volume 55, Issue 1, Part 3, Pages 376-387, January 2014.\u00a0doi:10.1016\/j.ijar.2013.10.006<\/a><\/span><\/li>\r\n
                        9. Ines Ketata, Lamia Sallemi, Fr\u00e9d\u00e9ric Morain-Nicolier Mohamed Ben Slima, Alexandre Cochet, Khalil Chtourou, Su Ruan & Ahmed Ben Hamida, \u00ab\u00a0Factor Analysis-based Approach for Early Uptake Automatic Quantification of Breast Cancer by 18F-FDG PET Image Sequence\u00a0\u00bb, Elsevier, Biomedical Signal Processing and Control<\/strong>, Vol.9. pp.19\u201331, 2014. \u00a0doi:10.1016\/j.bspc.2013.07.008<\/a><\/span><\/li>\r\n
                        10. Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan, \u00ab\u00a0Brain tumor segmentation from multiple MRI sequences using multiple kernel learning\u00a0\u00bb, IEEE-ICIP, Paris, October 2014.<\/span><\/li>\r\n
                        11. Paul Desbordes, Caroline Petitjean and Su Ruan, \u00ab\u00a03D automated lymphoma segmentation in PET images based on cellular automata\u00a0\u00bb, IPTA-2014, Paris, September 2014.<\/span><\/li>\r\n
                        12. Hongmei Mi, Caroline Petitjean, Pierre Vera, Su Ruan, \u00ab\u00a0Robust Feature Selection to Predict\u00a0Tumor Treatmen Outcome\u00a0\u00bb, \u00a0 Computational Methods for Molecular Imaging,\u00a0MICAAI workshop,\u00a0\u00a0Boston, September 2014.<\/span><\/li>\r\n
                        13. Said Ettaieb, Kamel Hamrouni, <\/strong>Su Ruan, \u201cMyocardium segmentation using a priori knowledge of shape and a spatial relation\u201d, 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, Morocco, April 2014.<\/span><\/li>\r\n
                        14. Yu Guo, Su Ruan, Paul Walker, Yuanming Feng, \u00ab\u00a0Prostate Cancer Segmentation from Multiparametric MRI Based on Fuzzy Bayesian Model \u00ab\u00a0, IEEE-ISBI, Beijing, April 2014.<\/span><\/li>\r\n
                        15. D. Grosgeorge, C. Petitjean, S. Ruan, \u00ab\u00a0Joint Segmentation of Right and Left Cardiac Ventricles Using Multi-Label Graph Cut\u00a0\u00bb, IEEE-ISBI, Beijing, April 2014.<\/span><\/li>\r\n
                        16. Hongmei Mi, Caroline Petitjean, Pierre Vera, Bernard Dubray, Su Ruan, \u00ab\u00a0Automatic Lung Tumor Segmentation on PET Images Based on Random Walks and Tumor Growth Model\u00a0\u00bb, IEEE-ISBI, Beijing, April 2014.<\/span><\/li>\r\n
                        17. Etta\u00efeb, S., Hamrouni, K., Ruan, S., \u201cStatistical Models of Shape and spatial relation-application to hippocampus segmentation\u201d, VISAPP 2014 – Proceedings of the 9th International Conference on Computer Vision Theory and Applications, Volume 1, 2014, Pages 448-455.<\/span><\/li>\r\n<\/ol>\r\n

                          2013<\/em><\/strong><\/p>\r\n

                            \r\n
                          1. Damien Grosgeorge, Caroline Petitjean, Bernard Dubray, and Su Ruan, \u00ab\u00a0Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut\u00a0\u00bb,\u00a0 Computational and Mathematical Methods in Medicine<\/strong>, Volume 2013,\u00a0<\/span>Article ID 547897, 6 pages, 2013.\u00a0\u00a0http:\/\/dx.doi.org\/10.1155\/2013\/547897<\/a>\u00a0<\/span><\/li>\r\n
                          2. D. Grosgeorge, C. Petitjean, J.-N. Dacher, S. Ruan, \u00ab\u00a0Graph cut segmentation with a statistical shape model in cardiac MRI\u00a0\u00bb, Elsevier, Computer Vision and Image Understanding<\/strong>, Vol.117, pp.1027-1035, 2013.\u00a0doi:10.1016\/j.cviu.2013.01.014<\/a><\/span><\/li>\r\n
                          3. P. Vera, R. Modzelewski, S. Hapdey, P. Gouel, H. Tilly, F. Jardin, S. Ruan, et I. Gardin. \u00ab\u00a0Does enhanced CT influence the biological GTV measurement on FDG-PET images?\u00a0\u00bb, Elsevier, Radiotherapy and Oncology,<\/strong> 108: 86–90, 2013. doi:10.1016\/j.radonc.2013.03.024<\/a><\/span><\/li>\r\n
                          4. XiangBo Lin, Su Ruan, Tian Shuang Qiu and DongMei Guo, \u00ab\u00a0Non-rigid Medical Image Registration Based on Mesh Deformation Constraints\u00a0\u00bb, Computational and Mathematical Methods in Medicine<\/strong>, Volume 2013, Article ID 373082, 8 pages, 2013.\u00a0http:\/\/dx.doi.org\/10.1155\/2013\/373082<\/a><\/span><\/li>\r\n
                          5. Hongmei Mi, Caroline Petitjean, Su Ruan, Pierre Vera, Bernard Dubray, \u00ab\u00a0Predicting lung tumor evolution during radiotherapy from PET images using a patient specific model\u00a0\u00bb, IEEE-ISBI, San Francisco, April 2013.<\/span><\/li>\r\n
                          6. Yu Guo, Su Ruan, \u201cSignal Separation with A Priori Knowledge Using Sparse Representation\u201d, Book chapter<\/em><\/strong>, In Amitava Chatterjee, Hadi Nobahari,and Patrick Siarry, editors, Advances in Heuristic Signal Processing and Applications, pp 315-332, Springer, 2013.<\/span><\/li>\r\n<\/ol>\r\n

                            2012<\/em><\/strong><\/p>\r\n

                              \r\n
                            1. Beno\u00eet Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera and Su Ruan, \u00ab\u00a0Segmentation of Biological Target Volumes on Multi-tracer PET Images Based on Information Fusion for Achieving Dose Painting in Radiotherapy\u00a0\u00bb, \u00a0MICCAI\u20192012, pp.545-549, Nice, France, Sept. 2012.<\/span><\/li>\r\n
                            2. D. Grosgeorge, C. Petitjean, S. Ruan, J. Caudron, et J. Dacher, \u00ab\u00a0Right ventriclesegmentation by graph cut with shape prior\u00a0\u00bb, In3D Cardiovascular Imaging : a MICCAI segmentation challenge. France, 2012.<\/span><\/li>\r\n
                            3. Y. Guo, S. Ruan, J. Landr\u00e9, Y. Zhang1, X. Ming1 and Y. Feng, \u00ab\u00a0Localization of prostate cancer based on fuzzy fusion of multispectral MRI\u00a0\u00bb, World Congress on Medical Physics and Biomedical Engineering, pp. 1844-1846, Beijing, May 2012.<\/span><\/li>\r\n
                            4. Onoma D. P., Ruan S., Isabelle G., Monnehan G. A., Modzelewski R., Vera P., \u00ab\u00a03D random walk based segmentation for lung tumor delineation in pet imaging\u00a0\u00bb, IEEE-ISBI, Barcelona, May 2012.<\/span><\/li>\r\n
                            5. P. D Onoma,S \u00a0Ruan . G. A. Monnehan, S. Tureau, R. Modzelewski, P. Vera, G\u00a0Isabelle, \u00ab\u00a03D Random walk based segmentation for delineation of heterogeneous positive tissues in PET imaging\u00a0\u00bb, In Annual Meeting of the Society of Nuclear Medicine and Molecular Imaging. \u00c9tats-Unis, 2012.<\/span><\/li>\r\n
                            6. P. D Onoma,S \u00a0Ruan . G. A. Monnehan, S. Tureau, R. Modzelewski, P. Vera, et I. Gardin. \u00ab\u00a03D Random Walk based Segmentation to Delineate heterogeneous BTV on 18FDG-PET images\u00a0\u00bb. In International Conference on Molecular Imaging in Radiation Oncology (MIRO). Autriche, 2012.<\/span><\/li>\r\n
                            7. Lelandais B., Gardin I., Mouchard L., Vera P., Ruan S., \u00ab\u00a0Using belief function theory to deal with uncertainties and imprecisions in image processing\u00a0\u00bb, The 2nd International Conference on Belief Functions, Compiegne, May, 2012.<\/span><\/li>\r\n<\/ol>\r\n

                              2011<\/em><\/strong><\/p>\r\n

                                \r\n
                              1. N. Zhang, S. Ruan, S. Lebonvallet, Q. Liao and Y. Zhu, \u00ab\u00a0Kernel Feature Selection to Fuse Multi-spectral MRI Images for Brain Tumor Segmentation\u00a0\u00bb, Elsevier, Computer Vision and Image Understanding<\/strong>, Vol.115 (2), pp.256-269, 2011. \u00a0doi:10.1016\/j.cviu.2010.09.007<\/a><\/span><\/li>\r\n
                              2. Y. Guo, S. Ruan, J. Landr\u00e9 et P. Walker, \u00ab\u00a0A Priori Knowledge Based Frequency-domain Quantification of Prostate Magnetic Resonance Spectroscopy\u00a0\u00bb, Elsevier, Biomedical Signal Processing and Control<\/strong>, vol.6(1), pp.13-20, 2011. \u00a0doi:10.1016\/j.bspc.2013.07.008<\/a><\/span><\/li>\r\n
                              3. S. Ruan, N. Zhang, Q. Liao and Y. Zhu, \u00ab\u00a0Image fusion for following-up brain tumor evolution\u00a0\u00bb, IEEE-ISBI, Chicago, USA, April 2011.<\/span><\/li>\r\n<\/ol>\r\n

                                2010<\/em><\/strong><\/p>\r\n

                                  \r\n
                                1. X. Lin, T. Qiu, F. Morain-Nicolier, S. Ruan, \u00ab\u00a0A Topology Preserving Non-Rigid Registration Algorithm with Integration Shape Knowledge to Segment Brain Subcortical Structures from MRI Images\u00a0\u00bb, Elsevier, Pattern Recognition<\/strong>, Vol.43(7), pp.2418-2427, 2010.\u00a0doi:10.1016\/j.patcog.2010.01.012<\/a><\/span><\/li>\r\n
                                2. Y. Guo, S. Ruan, J. Landr\u00e9, J-M. Constans, \u00ab\u00a0A Sparse Representation Method for Magnetic Resonance Spectroscopy Quantification\u00a0\u00bb, IEEE Transactions on Biomedical Engineering<\/strong>, 57(7):1620-1627, 2010.\u00a0DOI:<\/span> 10.1109\/TBME.2010.2045123<\/a><\/li>\r\n<\/ol>\r\n","protected":false},"excerpt":{"rendered":"

                                  \u00a0 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 Ling Huang, Su Ruan, Pierre Decazes, Thierry Den\u0153ux, \u201cDeep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation\u201d, … Continuer la lecture →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":20,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"class_list":["post-7","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/pages\/7","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/comments?post=7"}],"version-history":[{"count":281,"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/pages\/7\/revisions"}],"predecessor-version":[{"id":671,"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/pages\/7\/revisions\/671"}],"wp:attachment":[{"href":"https:\/\/pagesperso.litislab.fr\/suruan\/wp-json\/wp\/v2\/media?parent=7"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}