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2020 – today
- 2025
[j69]Aghiles Kebaili
, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation. Comput. Medical Imaging Graph. 123: 102532 (2025)- [j68]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025) - [j67]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. Pattern Recognit. Lett. 187: 93-99 (2025) - [i37]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality Imputation. CoRR abs/2501.12840 (2025) - 2024
- [j66]Fethi Ghazouani, Pierre Vera, Su Ruan:
Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention. Int. J. Comput. Assist. Radiol. Surg. 19(2): 273-281 (2024) - [j65]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes. Neurocomputing 606: 128360 (2024) - [j64]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Medical Image Anal. 97: 103223 (2024) - [c94]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Self-prior Guided Mamba-UNet Networks for Medical Image Super-Resolution. ICPR (11) 2024: 160-174 - [c93]Thibaud Brochet, Kangfu Han, Jiale Cheng, Fenqiang Zhao, Jérôme Lapuyade-Lahorgue, Su Ruan, Yi-Fang Tu, Sheng-Che Hung, Gang Li:
Neonatal Hypoxic Ischemic Encephalopathy Severity Grading Using Multimodal Swin Transformer. ISBI 2024: 1-4 - [c92]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. ISBI 2024: 1-5 - [c91]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Deform-Mamba Network for MRI Super-Resolution. MICCAI (7) 2024: 242-252 - [i36]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. CoRR abs/2406.05421 (2024) - [i35]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes. CoRR abs/2406.11659 (2024) - [i34]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Deform-Mamba Network for MRI Super-Resolution. CoRR abs/2407.05969 (2024) - [i33]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution. CoRR abs/2407.05993 (2024) - [i32]Zacharia Mesbah, Léo Mottay, Romain Modzelewski, Pierre Decazes, Sébastien Hapdey, Su Ruan, Sébastien Thureau:
AutoPETIII: The Tracer Frontier. What Frontier? CoRR abs/2410.02807 (2024) - [i31]Ling Huang, Yucheng Xing, Qika Lin, Su Ruan, Mengling Feng:
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers. CoRR abs/2412.01215 (2024) - 2023
- [j63]Tongxue Zhou, Su Ruan, Haigen Hu:
A literature survey of MR-based brain tumor segmentation with missing modalities. Comput. Medical Imaging Graph. 104: 102167 (2023) - [j62]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. Comput. Medical Imaging Graph. 106: 102218 (2023) - [j61]Ling Huang, Su Ruan, Thierry Denoeux:
Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing 535: 40-52 (2023) - [j60]Ling Huang, Su Ruan, Thierry Denoeux:
Application of belief functions to medical image segmentation: A review. Inf. Fusion 91: 737-756 (2023) - [j59]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan:
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. J. Imaging 9(4): 81 (2023) - [c90]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Hua Li, Pierre Vera, Pierre Decazes, Su Ruan:
Prediction of Head-Neck Cancer Recurrence from Pet/CT Images with Havrda-Charvat Entropy. IPTA 2023: 1-5 - [c89]Lixin Zhang, Yulun Sun, Pierre Decazes, Su Ruan, Yu Guo, Hui Yu:
One-shot Learning for DLBCL Segmentation in Whole Body PET/CT Images. ISBI 2023: 1-4 - [c88]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 2023: 32-40 - [i30]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. CoRR abs/2304.13725 (2023) - [i29]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan:
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. CoRR abs/2307.13125 (2023) - [i28]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - [i27]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods. CoRR abs/2310.06873 (2023) - [i26]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. CoRR abs/2311.10472 (2023) - 2022
- [j58]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Multi-task multi-scale learning for outcome prediction in 3D PET images. Comput. Biol. Medicine 151(Part): 106208 (2022) - [j57]Zhengshan Huang, Yu Guo, Ning Zhang, Xian Huang, Pierre Decazes, Stéphanie Becker, Su Ruan:
Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Comput. Biol. Medicine 151(Part): 106230 (2022) - [j56]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 24(4): 436 (2022) - [j55]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat, Vincent Grégoire, Pierre Vera, Su Ruan:
Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24, 436. Entropy 24(5): 685 (2022) - [j54]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [j53]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction. J. Imaging 8(5): 130 (2022) - [j52]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-Attention fusion guided multi-modal segmentation network. Pattern Recognit. 124: 108417 (2022) - [j51]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. Pattern Recognit. 124: 108452 (2022) - [j50]Tongxue Zhou, Pierre Vera, Stéphane Canu, Su Ruan:
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. Pattern Recognit. Lett. 158: 125-132 (2022) - [c87]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. ICPR 2022: 4414-4419 - [c86]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat, Vincent Grégoire, Pierre Vera, Su Ruan:
Deep Learning Based Radiomics to Predict Treatment Response Using Multi-datasets. MICAD 2022: 431-440 - [c85]Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joël Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca Saint-Esteven, Abdallah Sherif Radwan Mohamed, Mohamed A. Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem A. Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge:
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. HECKTOR@MICCAI 2022: 1-30 - [c84]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. MICCAI (5) 2022: 401-411 - [i25]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - [i24]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images. CoRR abs/2203.00641 (2022) - [i23]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction. CoRR abs/2203.11943 (2022) - [i22]Ling Huang, Su Ruan:
Application of belief functions to medical image segmentation: A review. CoRR abs/2205.01733 (2022) - [i21]Zong Fan, Xiaohui Zhang, Jacob A. Gasienica, Jennifer Potts, Su Ruan, Wade Thorstad, Hiram Gay, Xiaowei Wang, Hua Li:
A novel adversarial learning strategy for medical image classification. CoRR abs/2206.11501 (2022) - [i20]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence fusion with contextual discounting for multi-modality medical image segmentation. CoRR abs/2206.11739 (2022) - 2021
- [j49]Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li:
A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers. Bioinform. 37(19): 3106-3114 (2021) - [j48]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. Neurocomputing 466: 102-112 (2021) - [j47]Tongxue Zhou, Stéphane Canu, Su Ruan:
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int. J. Imaging Syst. Technol. 31(1): 16-27 (2021) - [j46]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities. IEEE Trans. Image Process. 30: 4263-4274 (2021) - [c83]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c82]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 Classification with Deep Neural Network and Belief Functions. BIBE 2021: 3:1-3:4 - [c81]Ling Huang, Su Ruan, Thierry Denoeux:
Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. ISBI 2021: 160-164 - [c80]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
A Dual Supervision Guided Attentional Network for Multimodal MR Brain Tumor Segmentation. MICAD 2021: 3-11 - [c79]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [c78]Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li:
Deep learning-based multi-class COVID-19 classification with x-ray images. Image-Guided Procedures 2021 - [i19]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 classification with deep neural network and belief functions. CoRR abs/2101.06958 (2021) - [i18]Ling Huang, Su Ruan, Thierry Denoeux:
Belief function-based semi-supervised learning for brain tumor segmentation. CoRR abs/2102.00097 (2021) - [i17]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. CoRR abs/2102.03111 (2021) - [i16]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Sébastien Bougleux, Mathieu Salaün, Su Ruan:
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy. CoRR abs/2104.05450 (2021) - [i15]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities. CoRR abs/2104.06231 (2021) - [i14]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i13]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021) - [i12]Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim:
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics. CoRR abs/2110.10332 (2021) - [i11]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-attention Fusion Guided Multi-modal Segmentation Network. CoRR abs/2111.01623 (2021) - [i10]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. CoRR abs/2111.04735 (2021) - [i9]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. CoRR abs/2112.10368 (2021) - 2020
- [j45]Amine Amyar, Romain Modzelewski, Hua Li, Su Ruan:
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput. Biol. Medicine 126: 104037 (2020) - [j44]Tongxue Zhou, Stéphane Canu, Su Ruan:
Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation. Comput. Medical Imaging Graph. 86: 101811 (2020) - [j43]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian. Digit. Signal Process. 101: 102703 (2020) - [j42]Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen:
Corrections to "Medical Image Synthesis With Deep Convolutional Adversarial Networks". IEEE Trans. Biomed. Eng. 67(9): 2706 (2020) - [c77]Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski:
RADIOGAN: Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images. ICBRA 2020: 28-33 - [c76]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. ICPR 2020: 10243-10250 - [c75]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Sébastien Bougleux, Mathieu Salaün, Su Ruan:
Deep learning based automatic detection of uninformative images in pulmonary optical endomicroscopy. IPTA 2020: 1-5 - [c74]Zoé Lambert, Caroline Petitjean, Bernard Dubray, Su Ruan:
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images. IPTA 2020: 1-6 - [c73]Yu Guo, Pierre Decazes, Stéphanie Becker, Hua Li, Su Ruan:
Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction. ISBI 2020: 1-4 - [c72]Tongxue Zhou, Su Ruan, Yu Guo, Stéphane Canu:
A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation. ISBI 2020: 377-380 - [c71]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. ISBI 2020: 1197-1200 - [c70]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation. MICCAI (4) 2020: 533-541 - [i8]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Weakly Supervised PET Tumor Detection Using Class Response. CoRR abs/2003.08337 (2020) - [i7]Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski:
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images. CoRR abs/2003.08663 (2020) - [i6]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain tumor segmentation with missing modalities via latent multi-source correlation representation. CoRR abs/2003.08870 (2020) - [i5]Tongxue Zhou, Stéphane Canu, Su Ruan:
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. CoRR abs/2004.06673 (2020) - [i4]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. CoRR abs/2004.10664 (2020)
2010 – 2019
- 2019
- [j41]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3-4: 100004 (2019) - [j40]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. Int. J. Comput. Assist. Radiol. Surg. 14(10): 1715-1724 (2019) - [j39]Fan Wang, Chunfeng Lian, Pierre Vera, Su Ruan:
Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG-PET images. Multim. Syst. 25(2): 127-133 (2019) - [j38]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Mixed Integer Programming For Sparse Coding: Application to Image Denoising. IEEE Trans. Computational Imaging 5(3): 354-365 (2019) - [j37]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. IEEE Trans. Image Process. 28(2): 755-766 (2019) - [c69]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. BIBM 2019: 1194-1196 - [c68]Haigen Hu, Chao Du, Pierre Decazes, Pierre Vera, Su Ruan:
A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs. BIBM 2019: 2413-2420 - [c67]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. CISP-BMEI 2019: 1-5 - [c66]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 - [e1]Caroline Petitjean, Su Ruan, Zoé Lambert, Bernard Dubray:
Proceedings of the 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, SegTHOR@ISBI 2019, April 8, 2019. CEUR Workshop Proceedings 2349, CEUR-WS.org 2019 [contents] - [i3]Zoé Lambert, Caroline Petitjean, Bernard Dubray, Su Ruan:
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images. CoRR abs/1912.05950 (2019) - 2018
- [j36]Yuntao Yu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Isabelle Gardin, Pierre Vera, Su Ruan:
Semi-automatic lymphoma detection and segmentation using fully conditional random fields. Comput. Medical Imaging Graph. 70: 1-7 (2018) - [j35]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 images. Medical Image Anal. 47: 68-80 (2018) - [j34]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Trans. Biomed. Eng. 65(1): 21-30 (2018) - [j33]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 Trans. Biomed. Eng. 65(12): 2720-2730 (2018) - [c65]Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan:
Feature selection and classification using multiple kernel learning for brain tumor segmentation. ATSIP 2018: 1-5 - [c64]Jierui Zha, Pierre Decazes, Jérôme Lapuyade, Abderrahim Elmoataz, Su Ruan:
3D lymphoma detection in PET-CT images with supervoxel and CRFs. IPTA 2018: 1-5 - [c63]Chunfeng Lian, Hua Li, Pierre Vera, Su Ruan:
Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion. ISBI 2018: 220-223 - [c62]Jian Wu, Su Ruan, Chunfeng Lian, Sasa Mutic, Mark A. Anastasio, Hua Li:
Active learning with noise modeling for medical image annotation. ISBI 2018: 298-301 - [c61]Jian Wu, Su Ruan, Thomas R. Mazur, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Chunfeng Lian, H. Michael Gach, Sasa Mutic, Maria Thomas, Mark A. Anastasio, Hua Li:
Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. ISBI 2018: 1153-1156 - [c60]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
K-SVD with a Real ℓ0 Optimization: Application to Image Denoising. MLSP 2018: 1-6 - 2017
- [j32]Paul Desbordes, Su Ruan, Romain Modzelewski, Vauclin Sébastien, Pierre Vera, Isabelle Gardin:
Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Comput. Medical Imaging Graph. 60: 42-49 (2017) - [j31]Jérôme Lapuyade-Lahorgue, Jing-Hao Xue, Su Ruan:
Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions. IEEE Trans. Image Process. 26(7): 3187-3195 (2017) - [c59]Chunfeng Lian, Su Ruan, Thierry Denoeux, Yu Guo, Pierre Vera:
Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images. ICIP 2017: 4447-4451 - [c58]Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan:
Fully automated esophagus segmentation with a hierarchical deep learning approach. ICSIPA 2017: 503-506 - [c57]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. ISBI 2017: 1003-1006 - [c56]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. ISBI 2017: 1177-1180 - [c55]Yuntao Yu, Pierre Decazes, Isabelle Gardin, Pierre Vera, Su Ruan:
3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs. CMMI/RAMBO/SWITCH@MICCAI 2017: 3-12 - [c54]Roger Trullo, Caroline Petitjean, Dong Nie, Dinggang Shen, Su Ruan:
Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures. DLMIA/ML-CDS@MICCAI 2017: 21-29 - [c53]Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen:
Medical Image Synthesis with Context-Aware Generative Adversarial Networks. MICCAI (3) 2017: 417-425 - [i2]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Une véritable approche $\ell_0$ pour l'apprentissage de dictionnaire. CoRR abs/1709.05937 (2017) - 2016
- [j30]Damien Grosgeorge, Caroline Petitjean, Su Ruan:
Multilabel statistical shape prior for image segmentation. IET Image Process. 10(10): 710-716 (2016) - [j29]Chunfeng Lian, Su Ruan, Thierry Denoeux, Fabrice Jardin, Pierre Vera:
Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Medical Image Anal. 32: 257-268 (2016) - [j28]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Dissimilarity Metric Learning in the Belief Function Framework. IEEE Trans. Fuzzy Syst. 24(6): 1555-1564 (2016) - [c52]Chunfeng Lian, Su Ruan, Thierry Denoeux:
Joint Feature Transformation and Selection Based on Dempster-Shafer Theory. IPMU (1) 2016: 253-261 - [c51]Kevin Gosse, Stéphanie Jehan-Besson, François Lecellier, Su Ruan:
Comparison of 2D and 3D region-based deformable models and random walker methods for PET segmentation. IPTA 2016: 1-7 - [c50]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Robust Cancer Treatment Outcome Prediction Dealing with Small-Sized and Imbalanced Data from FDG-PET Images. MICCAI (2) 2016: 61-69 - [i1]Dong Nie, Roger Trullo, Caroline Petitjean, Su Ruan, Dinggang Shen:
Medical Image Synthesis with Context-Aware Generative Adversarial Networks. CoRR abs/1612.05362 (2016) - 2015
- [j27]Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan:
Robust feature selection to predict tumor treatment outcome. Artif. Intell. Medicine 64(3): 195-204 (2015) - [j26]Caroline Petitjean, Maria A. Zuluaga, Wenjia Bai, Jean-Nicolas Dacher, Damien Grosgeorge, Jérôme Caudron, Su Ruan, Ismail Ben Ayed, Manuel Jorge Cardoso, Hsiang-Chou Chen, Daniel Jimenez-Carretero, María J. Ledesma-Carbayo, Christos Davatzikos, Jimit Doshi, Güray Erus, Oskar M. O. Maier, Cyrus M. S. Nambakhsh, Yangming Ou, Sébastien Ourselin, Chun-Wei Peng, Nicholas S. Peters, Terry M. Peters, Martin Rajchl, Daniel Rueckert, Andrés Santos, Wenzhe Shi, Ching-Wei Wang, Haiyan Wang, Jing Yuan:
Right ventricle segmentation from cardiac MRI: A collation study. Medical Image Anal. 19(1): 187-202 (2015) - [j25]Hongmei Mi, Caroline Petitjean, Pierre Vera, Su Ruan:
Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images. Medical Image Anal. 23(1): 84-91 (2015) - [j24]Chunfeng Lian, Su Ruan, Thierry Denoeux:
An evidential classifier based on feature selection and two-step classification strategy. Pattern Recognit. 48(7): 2318-2327 (2015) - [c49]Saïd Ettaïeb, Kamel Hamrouni, Su Ruan:
Modelling and Tracking of Deformable Structures in Medical Images. ICIG (2) 2015: 475-490 - [c48]Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre Vera:
Outcome prediction in tumour therapy based on Dempster-Shafer theory. ISBI 2015: 63-66 - [c47]Maxime Guinin, Su Ruan, Lamyaa Nkhali, Bernard Dubray, Laurent Massoptier, Isabelle Gardin:
Segmentation of pelvic organs at risk using superpixels and graph diffusion in prostate radiotherapy. ISBI 2015: 1564-1567 - [c46]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy. MICCAI (3) 2015: 695-702 - 2014
- [j23]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 images sequence. Biomed. Signal Process. Control. 9: 19-31 (2014) - [j22]D. P. Onoma, Su Ruan, Sébastien Thureau, Lamyaa Nkhali, Romain Modzelewski, G. A. Monnehan, Pierre Vera, Isabelle Gardin:
Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm. Comput. Medical Imaging Graph. 38(8): 753-763 (2014) - [j21]Benoît Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan:
Dealing with uncertainty and imprecision in image segmentation using belief function theory. Int. J. Approx. Reason. 55(1): 376-387 (2014) - [j20]Pierre Buyssens, Isabelle Gardin, Su Ruan, Abderrahim Elmoataz:
Eikonal-based region growing for efficient clustering. Image Vis. Comput. 32(12): 1045-1054 (2014) - [j19]Benoît Lelandais, Su Ruan, Thierry Denoeux, Pierre Vera, Isabelle Gardin:
Fusion of multi-tracer PET images for dose painting. Medical Image Anal. 18(7): 1247-1259 (2014) - [j18]Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan:
Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET. IEEE Trans. Medical Imaging 33(4): 995-1003 (2014) - [c45]Ines Ketata, Lamia Sallemi, Mohamed Ben Slima, Ahmed Ben Hamida, Frédéric Morain-Nicolier, Su Ruan, Alexandre Cochet, Khalil Chtourou:
Advanced approach for PET breast cancer segmentation based on FAMIS methodology. ATSIP 2014: 219-224 - [c44]Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan:
Brain tumor segmentation from multiple MRI sequences using multiple kernel learning. ICIP 2014: 1887-1891 - [c43]Saïd Ettaïeb, Kamel Hamrouni, Su Ruan:
Myocardium segmentation using a priori knowledge of shape and a spatial relation. ICMCS 2014: 380-384 - [c42]Paul Desbordes, Caroline Petitjean, Su Ruan:
3D automated lymphoma segmentation in PET images based on cellular automata. IPTA 2014: 23-28 - [c41]Damien Grosgeorge, Caroline Petitjean, Su Ruan:
Joint segmentation of right and left cardiac ventricles using multi-label graph cut. ISBI 2014: 429-432 - [c40]Yu Guo, Su Ruan, Paul Walker, Yuanming Feng:
Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model. ISBI 2014: 866-869 - [c39]Hongmei Mi, Caroline Petitjean, Bernard Dubray, Pierre Vera, Su Ruan:
Automatic lung tumor segmentation on PET images based on random walks and tumor growth model. ISBI 2014: 1385-1388 - [c38]Saïd Ettaïeb, Kamel Hamrouni, Su Ruan:
Statistical Models of Shape and Spatial Relation-application to Hippocampus Segmentation. VISAPP (1) 2014: 448-455 - 2013
- [j17]Xiangbo Lin, Su Ruan, Tianshuang Qiu, Dongmei Guo:
Nonrigid Medical Image Registration Based on Mesh Deformation Constraints. Comput. Math. Methods Medicine 2013: 373082:1-373082:8 (2013) - [j16]Damien Grosgeorge, Caroline Petitjean, Bernard Dubray, Su Ruan:
Esophagus Segmentation from 3D CT Data Using Skeleton Prior-Based Graph Cut. Comput. Math. Methods Medicine 2013: 547897:1-547897:6 (2013) - [j15]Damien Grosgeorge, Caroline Petitjean, Jean-Nicolas Dacher, Su Ruan:
Graph cut segmentation with a statistical shape model in cardiac MRI. Comput. Vis. Image Underst. 117(9): 1027-1035 (2013) - [c37]Hongmei Mi, Caroline Petitjean, Su Ruan, Pierre Vera, Bernard Dubray:
Predicting lung tumor evolution during radiotherapy from PET images using a patient specific model. ISBI 2013: 1404-1407 - [p1]Yu Guo, Su Ruan:
Signal Separation with A Priori Knowledge Using Sparse Representation. Advances in Heuristic Signal Processing and Applications 2013: 315-332 - 2012
- [c36]Benoît Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan:
Using Belief Function Theory to Deal with Uncertainties and Imprecisions in Image Processing. Belief Functions 2012: 197-204 - [c35]Xiangbo Lin, Su Ruan:
Analyzing the intrinsic relations between the diffusion and fluid deformable registration methods. BMEI 2012: 200-203 - [c34]D. P. Onoma, Su Ruan, Isabelle Gardin, G. A. Monnehan, Romain Modzelewski, Pierre Vera:
3D random walk based segmentation for lung tumor delineation in PET imaging. ISBI 2012: 1260-1263 - [c33]Benoît Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan:
Segmentation of Biological Target Volumes on Multi-tracer PET Images Based on Information Fusion for Achieving Dose Painting in Radiotherapy. MICCAI (1) 2012: 545-552 - 2011
- [j14]Yu Guo, Su Ruan, Jérôme Landré, Paul Walker:
A priori knowledge based frequency-domain quantification of prostate Magnetic Resonance Spectroscopy. Biomed. Signal Process. Control. 6(1): 13-20 (2011) - [j13]Nan Zhang, Su Ruan, Stéphane Lebonvallet, Qingmin Liao, Yuemin Zhu:
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation. Comput. Vis. Image Underst. 115(2): 256-269 (2011) - [c32]Su Ruan, Nan Zhang, Qingmin Liao, Yuemin Zhu:
Image fusion for following-up brain tumor evolution. ISBI 2011: 281-284 - 2010
- [j12]Xiangbo Lin, Tianshuang Qiu, Frédéric Morain-Nicolier, Su Ruan:
A topology preserving non-rigid registration algorithm with integration shape knowledge to segment brain subcortical structures from MRI images. Pattern Recognit. 43(7): 2418-2427 (2010) - [j11]Su Ruan, Stéphane Lebonvallet:
Brain Magnetic Resonance Image. Stud. Inform. Univ. 8(4): 128-149 (2010) - [j10]Yu Guo, Su Ruan, Jérôme Landré, Jean-Marc Constans:
A Sparse Representation Method for Magnetic Resonance Spectroscopy Quantification. IEEE Trans. Biomed. Eng. 57(7): 1620-1627 (2010) - [c31]Xiaobing Li, Tianshuang Qiu, Stéphane Lebonvallet, Su Ruan:
An automatic method of brain tumor segmentation from MRI volume based on the symmetry of brain and level set method. ICDIP 2010: 754618 - [c30]Su Ruan, Nan Zhang, Stéphane Lebonvallet, Qingmin Liao, Yue Min Zhu:
Fusion and classification of multi-source images by SVM with selected features in a kernel space. IPTA 2010: 17-20 - [c29]Victor Chen, Su Ruan:
Graph cut segmentation technique for MRI brain tumor extraction. IPTA 2010: 284-287 - [c28]Frédéric Morain-Nicolier, Jérôme Landré, Su Ruan:
Binary pattern matching from a local dissimilarity measure. IPTA 2010: 417-420
2000 – 2009
- 2009
- [c27]Yu Guo, Su Ruan, Gilles Millon, Jean-Marc Constans:
Spectrum separation of Magnetic Resonance Spectroscopy based on sparse representation. ICASSP 2009: 453-456 - [c26]Jérôme Landré, Frédéric Morain-Nicolier, Su Ruan:
Ornamental Letters Image Classification Using Local Dissimilarity Maps. ICDAR 2009: 186-190 - [c25]Nan Zhang, Su Ruan, Stéphane Lebonvallet, Qingmin Liao, Yuemin Zhu:
Multi-kernel SVM based classification for brain tumor segmentation of MRI multi-sequence. ICIP 2009: 3373-3376 - 2008
- [j9]Étienne Baudrier, Frédéric Nicolier, Gilles Millon, Su Ruan:
Binary-image comparison with local-dissimilarity quantification. Pattern Recognit. 41(5): 1461-1478 (2008) - [c24]Xiangbo Lin, Tianshuang Qiu, Su Ruan, Frédéric Morain-Nicolier:
Segmentation of Brain Internal Structures Automatically Using Non-rigid Registration with Simultaneous Intensity and Geometric Match. ISDA (1) 2008: 525-530 - 2007
- [j8]Weibei Dou, Yuan Ren, Qian Wu, Su Ruan, Yanping Chen, Daniel Bloyet, Jean-Marc Constans:
Fuzzy kappa for the agreement measure of fuzzy classifications. Neurocomputing 70(4-6): 726-734 (2007) - [j7]Weibei Dou, Su Ruan, Yanping Chen, Daniel Bloyet, Jean-Marc Constans:
A framework of fuzzy information fusion for the segmentation of brain tumor tissues on MR images. Image Vis. Comput. 25(2): 164-171 (2007) - [c23]Su Ruan, Stéphane Lebonvallet, Abderrahim Merabet, Jean-Marc Constans:
Tumor Segmentation from a Multispectral MRI Images by Using Support Vector Machine Classification. ISBI 2007: 1236-1239 - [c22]Su Ruan, Jonathan Bailleul:
MRI segmentation using multifractal analysis and MRF models. VISAPP (Special Sessions) 2007: 101-106 - [c21]Stéphane Lebonvallet, Sonia Khatchadourian, Su Ruan:
Automated tumor segmentation using level set methods. VISAPP (Special Sessions) 2007: 128-133 - 2006
- [c20]Étienne Baudrier, Gilles Millon, Frédéric Nicolier, Su Ruan:
A fast binary-image comparison method with local-dissimilarity quantification. ICPR (3) 2006: 216-219 - [c19]Kun Peng, Liming Chen, Su Ruan:
A Smart Identification Card System Using Facial Biometric: From Architecture to Application. UIC 2006: 61-70 - 2005
- [c18]Kun Peng, Liming Chen, Su Ruan:
A novel scheme of face verification using active appearance models. AVSS 2005: 247-252 - [c17]Weibei Dou, Yuan Ren, Yanping Chen, Su Ruan, Daniel Bloyet, Jean-Marc Constans:
Histogram-Based Generation Method of Membership Function for Extracting Features of Brain Tissues on MRI Images. FSKD (1) 2005: 189-194 - [c16]Kun Peng, Liming Chen, Su Ruan, Georgy Kukharev:
A Robust and Efficient Algorithm for Eye Detection on Gray Intensity Face. ICAPR (2) 2005: 302-308 - 2004
- [c15]Étienne Baudrier, Gilles Millon, Frédéric Nicolier, Su Ruan:
A new similarity measure using hausdorff distance map. ICIP 2004: 669-672 - [c14]Jonathan Bailleul, Su Ruan, Daniel Bloyet, Barbara Romaniuk:
Segmentation of anatomical structures from 3D brain MRI using automatically-built statistical shape models. ICIP 2004: 2741-2744 - [c13]Hua Li, Abderrahim Elmoataz, Jalal Fadili, Su Ruan, Barbara Romaniuk:
3d medical image segmentation approach based on multi-label front propagation. ICIP 2004: 2925-2928 - [c12]Hua Li, Abderrahim Elmoataz, Mohamed-Jalal Fadili, Su Ruan:
A Multi-Label Front Propagation Approach for Object Segmentation. ICPR (1) 2004: 600-603 - [c11]Weibei Dou, Su Ruan, Daniel Bloyet, Jean-Marc Constans, Yanping Chen:
Segmentation based on information fusion applied to brain tissue on MRI. Image Processing: Algorithms and Systems 2004: 492-503 - [c10]Hua Li, Abderrahim Elmoataz, Mohamed-Jalal Fadili, Su Ruan:
Dual Front Evolution Model and Its Application in Medical Imaging. MICCAI (1) 2004: 103-110 - [c9]Qingmin Liao, Yingying Deng, Weibei Dou, Su Ruan, Daniel Bloyet:
Possibilistic-clustering-based MR brain image segmentation with accurate initialization. VCIP 2004 - 2003
- [c8]Jonathan Bailleul, Su Ruan, Daniel Bloyet:
Automatic atlas-based building of point distribution model for segmentation of anatomical structures from brain MRI. ISSPA (2) 2003: 629-630 - [c7]Weibei Dou, Su Ruan, Qingmin Liao, Daniel Bloyet, Jean-Marc Constans:
Knowledge based fuzzy information fusion applied to classification of abnormal brain tissues from MRI. ISSPA (1) 2003: 681-684 - [c6]Weibei Dou, Su Ruan, Qingmin Liao, Daniel Bloyet, Jean-Marc Constans, Yanping Chen:
Fuzzy Information Fusion Scheme Used to Segment Brain Tumor from MR Images. WILF 2003: 208-215 - 2002
- [j6]Su Ruan, Bruno Moretti, Jalal Fadili, Daniel Bloyet:
Fuzzy Markovian Segmentation in Application of Magnetic Resonance Images. Comput. Vis. Image Underst. 85(1): 54-69 (2002) - [c5]Su Ruan, Daniel Bloyet, Marinette Revenu, Weibei Dou, Qingmin Liao:
Cerebral magnetic resonance image segmentation using fuzzy Markov Random Fields. ISBI 2002: 237-240 - 2001
- [j5]Mohamed-Jalal Fadili, Su Ruan, Daniel Bloyet, Bernard Mazoyer:
On the number of clusters and the fuzziness index for unsupervised FCA application to BOLD fMRI time series. Medical Image Anal. 5(1): 55-67 (2001) - [j4]Jing-Hao Xue, Su Ruan, Bruno Moretti, Marinette Revenu, Daniel Bloyet:
Knowledge-based segmentation and labeling of brain structures from MRI images. Pattern Recognit. Lett. 22(3/4): 395-405 (2001) - [c4]Su Ruan, Bruno Moretti, Jalal Fadili, Daniel Bloyet:
Segmentation of magnetic resonance images using fuzzy Markov random fields. ICIP (3) 2001: 1051-1054 - 2000
- [j3]Bruno Moretti, Mohamed-Jalal Fadili, Su Ruan, Daniel Bloyet, Bernard Mazoyer:
Phantom-based performance evaluation: Application to brain segmentation from magnetic resonance images. Medical Image Anal. 4(4): 303-316 (2000) - [j2]Su Ruan, Cyril Jaggi, Jing-Hao Xue, Mohamed-Jalal Fadili, Daniel Bloyet:
Brain Tissue Classification of Magnetic Resonance Images Using Partial Volume Modeling. IEEE Trans. Medical Imaging 19(12): 1179-1187 (2000) - [c3]Jing-Hao Xue, Su Ruan, Bruno Moretti, Marinette Revenu, Daniel Bloyet, Wilfried Philips:
Fuzzy Modeling of Knowledge for MRI Brain Structure Segmentation. ICIP 2000: 617-620 - [c2]Su Ruan, Mohamed-Jalal Fadili, Daniel Bloyet, Jing-Hao Xue:
Unsupervised Segmentation of Three-Dimensional Brain Images. ICPR 2000: 3409-3412
1990 – 1999
- 1995
- [c1]Su Ruan, Cyril Jaggi, Jean-Marc Constans, Daniel Bloyet:
Detection of Brain Activation from MRI Data by Likelihood-Ratio Test. CVRMed 1995: 341-350 - 1994
- [j1]Su Ruan, Alain Bruno, Jean-Louis Coatrieux:
Three-dimensional motion and reconstruction of coronary arteries from biplane cineangiography. Image Vis. Comput. 12(10): 683-689 (1994)
Coauthor Index
Jérôme Lapuyade-Lahorgue
aka: Jérôme Lapuyade
aka: Jérôme Lapuyade
Frédéric Morain-Nicolier
aka: Frédéric Nicolier
aka: Frédéric Nicolier
[j69] [j67] [i37] [j66] [j65] [c94] [c92] [c91] [i36] [i35] [i34] [i33] [c90] [c88] [i26] [j58] [j56] [j55] [j53] [j52] [j50] [c86] [c85] [c84] [i24] [i23] [i20] [j48] [j46] [c80] [i17] [i15] [i11] [i10] [c77] [c76] [c71] [c70] [i8] [i7] [i6] [j40] [j39] [j37] [c69] [c68] [c67] [j36] [j34] [c63] [j32] [c59] [c56] [c55] [j29] [c50] [j27] [j25] [c48] [c46] [j22] [j21] [j19] [j18] [c39] [c37] [c36] [c34] [c33]
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