The Experts below are selected from a list of 13788 Experts worldwide ranked by ideXlab platform
Nikos Paragios - One of the best experts on this subject based on the ideXlab platform.
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Joint Tumor segmentation and dense deformable registration of brain mr images
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
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MICCAI (2) - Joint Tumor segmentation and dense deformable registration of brain MR images
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
Sarah Parisot - One of the best experts on this subject based on the ideXlab platform.
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Joint Tumor segmentation and dense deformable registration of brain mr images
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
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MICCAI (2) - Joint Tumor segmentation and dense deformable registration of brain MR images
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
Pierre Vera - One of the best experts on this subject based on the ideXlab platform.
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Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions
IEEE Transactions on Image Processing, 2019Co-Authors: Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre VeraAbstract:Precise delineation of target Tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many existing automatic/semi-automatic methods still perform Tumor segmentation on mono-modal images. In this paper, a co-clustering algorithm is proposed to concurrently segment 3D Tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance. The theory of belief functions is adopted in the proposed method to model, fuse, and reason with uncertain and imprecise knowledge from noisy and blurry PET-CT images. To ensure reliable segmentation for each modality, the distance metric for the quantification of clustering distortions and spatial smoothness is iteratively adapted during the clustering procedure. On the other hand, to encourage consistent segmentation between different modalities, a specific context term is proposed in the clustering objective function. Moreover, during the iterative optimization process, clustering results for the two distinct modalities are further adjusted via a belief-functions-based information fusion strategy. The proposed method has been evaluated on a data set consisting of 21 paired PET-CT images for non-small cell lung cancer patients. The quantitative and qualitative evaluations show that our proposed method performs well compared with the state-of-the-art methods.
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Joint Tumor Growth Prediction and Tumor Segmentation on Therapeutic Follow-up PET Images
Medical image analysis, 2015Co-Authors: Caroline Petitjean, Pierre Vera, Su RuanAbstract:Tumor response to treatment varies among patients. Patient-specific prediction of Tumor evolution based on medical images during the treatment can help to build and adapt patient’s treatment planning in a non-invasive way. Personalized Tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual’s images. The model parameters are often estimated by optimizing a cost function constructed based on the Tumor delineations. In this paper, we propose a Joint framework for Tumor growth prediction and Tumor segmentation in the context of patient’s therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for Tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic Tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of Tumor evolution. We evaluate our methods on 7 lung Tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained Tumor prediction and Tumor segmentation with manual Tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.
Su Ruan - One of the best experts on this subject based on the ideXlab platform.
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Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions
IEEE Transactions on Image Processing, 2019Co-Authors: Chunfeng Lian, Su Ruan, Thierry Denoeux, Pierre VeraAbstract:Precise delineation of target Tumor is a key factor to ensure the effectiveness of radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practice of radiation oncology, many existing automatic/semi-automatic methods still perform Tumor segmentation on mono-modal images. In this paper, a co-clustering algorithm is proposed to concurrently segment 3D Tumors in PET-CT images, considering that the two complementary imaging modalities can combine functional and anatomical information to improve segmentation performance. The theory of belief functions is adopted in the proposed method to model, fuse, and reason with uncertain and imprecise knowledge from noisy and blurry PET-CT images. To ensure reliable segmentation for each modality, the distance metric for the quantification of clustering distortions and spatial smoothness is iteratively adapted during the clustering procedure. On the other hand, to encourage consistent segmentation between different modalities, a specific context term is proposed in the clustering objective function. Moreover, during the iterative optimization process, clustering results for the two distinct modalities are further adjusted via a belief-functions-based information fusion strategy. The proposed method has been evaluated on a data set consisting of 21 paired PET-CT images for non-small cell lung cancer patients. The quantitative and qualitative evaluations show that our proposed method performs well compared with the state-of-the-art methods.
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Joint Tumor Growth Prediction and Tumor Segmentation on Therapeutic Follow-up PET Images
Medical image analysis, 2015Co-Authors: Caroline Petitjean, Pierre Vera, Su RuanAbstract:Tumor response to treatment varies among patients. Patient-specific prediction of Tumor evolution based on medical images during the treatment can help to build and adapt patient’s treatment planning in a non-invasive way. Personalized Tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual’s images. The model parameters are often estimated by optimizing a cost function constructed based on the Tumor delineations. In this paper, we propose a Joint framework for Tumor growth prediction and Tumor segmentation in the context of patient’s therapeutic follow ups. Throughout the treatment, a series of sequential positron emission tomography (PET) images are acquired for Tumor response monitoring. We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic Tumor segmentation method on PET images. Moreover, we propose an iterative scheme of RW, making the segmentation more performant. Furthermore, the obtained segmentation is applied to the process of model parameter estimation so as to get the model based prediction of Tumor evolution. We evaluate our methods on 7 lung Tumor patients, totaling 29 PET exams, under radiotherapy by comparing the obtained Tumor prediction and Tumor segmentation with manual Tumor delineation by expert. Our system produces promising results when compared to the state-of-the-art methods.
Stephane Chemouny - One of the best experts on this subject based on the ideXlab platform.
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Joint Tumor segmentation and dense deformable registration of brain mr images
Medical Image Computing and Computer-Assisted Intervention, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
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MICCAI (2) - Joint Tumor segmentation and dense deformable registration of brain MR images
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Inte, 2012Co-Authors: Sarah Parisot, Hugues Duffau, Stephane Chemouny, Nikos ParagiosAbstract:In this paper we propose a novel graph-based concurrent registration and segmentation framework. Registration is modeled with a pairwise graphical model formulation that is modular with respect to the data and regularization term. Segmentation is addressed by adopting a similar graphical model, using image-based classification techniques while producing a smooth solution. The two problems are coupled via a relaxation of the registration criterion in the presence of Tumors as well as a segmentation through a registration term aiming the separation between healthy and diseased tissues. Efficient linear programming is used to solve both problems simultaneously. State of the art results demonstrate the potential of our method on a large and challenging low-grade glioma data set.
