The Experts below are selected from a list of 4806 Experts worldwide ranked by ideXlab platform
A Murtha - One of the best experts on this subject based on the ideXlab platform.
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3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc. IEEE 11th International Conference on Computer Vision ICCV 2007, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical Model for tumor and normal tissue. We show that using a Conditional Model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative Models. Validation is performed by testing the method on several cancer patient MRI scans.
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{3D} Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc ICCV, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming\ntask performed manually by medical experts. Automating this process\nis challenging due to the high diversity in appearance of tumor tissue,\namong different patients and, in many cases, similarity between tumor\nand normal tissue. One other challenge is how to make use of prior\ninformation about the appearance of normal brain. In this paper we\npropose a variational brain tumor segmentation algorithm that extends\ncurrent approaches from texture segmentation by using a high dimensional\nfeature set calculated from MRI data and registered atlases. Using\nmanually segmented data we learn a statistical Model for tumor and\nnormal tissue. We show that using a Conditional Model to discriminate\nbetween normal and abnormal regions significantly improves the segmentation\nresults compared to traditional generative Models. Validation is\nperformed by testing the method on several cancer patient MRI scans.
Dana Cobzaş - One of the best experts on this subject based on the ideXlab platform.
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3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc. IEEE 11th International Conference on Computer Vision ICCV 2007, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical Model for tumor and normal tissue. We show that using a Conditional Model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative Models. Validation is performed by testing the method on several cancer patient MRI scans.
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{3D} Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc ICCV, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming\ntask performed manually by medical experts. Automating this process\nis challenging due to the high diversity in appearance of tumor tissue,\namong different patients and, in many cases, similarity between tumor\nand normal tissue. One other challenge is how to make use of prior\ninformation about the appearance of normal brain. In this paper we\npropose a variational brain tumor segmentation algorithm that extends\ncurrent approaches from texture segmentation by using a high dimensional\nfeature set calculated from MRI data and registered atlases. Using\nmanually segmented data we learn a statistical Model for tumor and\nnormal tissue. We show that using a Conditional Model to discriminate\nbetween normal and abnormal regions significantly improves the segmentation\nresults compared to traditional generative Models. Validation is\nperformed by testing the method on several cancer patient MRI scans.
Mark Schmidt - One of the best experts on this subject based on the ideXlab platform.
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3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc. IEEE 11th International Conference on Computer Vision ICCV 2007, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical Model for tumor and normal tissue. We show that using a Conditional Model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative Models. Validation is performed by testing the method on several cancer patient MRI scans.
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{3D} Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc ICCV, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming\ntask performed manually by medical experts. Automating this process\nis challenging due to the high diversity in appearance of tumor tissue,\namong different patients and, in many cases, similarity between tumor\nand normal tissue. One other challenge is how to make use of prior\ninformation about the appearance of normal brain. In this paper we\npropose a variational brain tumor segmentation algorithm that extends\ncurrent approaches from texture segmentation by using a high dimensional\nfeature set calculated from MRI data and registered atlases. Using\nmanually segmented data we learn a statistical Model for tumor and\nnormal tissue. We show that using a Conditional Model to discriminate\nbetween normal and abnormal regions significantly improves the segmentation\nresults compared to traditional generative Models. Validation is\nperformed by testing the method on several cancer patient MRI scans.
Martin Jägersand - One of the best experts on this subject based on the ideXlab platform.
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3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc. IEEE 11th International Conference on Computer Vision ICCV 2007, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical Model for tumor and normal tissue. We show that using a Conditional Model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative Models. Validation is performed by testing the method on several cancer patient MRI scans.
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{3D} Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc ICCV, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming\ntask performed manually by medical experts. Automating this process\nis challenging due to the high diversity in appearance of tumor tissue,\namong different patients and, in many cases, similarity between tumor\nand normal tissue. One other challenge is how to make use of prior\ninformation about the appearance of normal brain. In this paper we\npropose a variational brain tumor segmentation algorithm that extends\ncurrent approaches from texture segmentation by using a high dimensional\nfeature set calculated from MRI data and registered atlases. Using\nmanually segmented data we learn a statistical Model for tumor and\nnormal tissue. We show that using a Conditional Model to discriminate\nbetween normal and abnormal regions significantly improves the segmentation\nresults compared to traditional generative Models. Validation is\nperformed by testing the method on several cancer patient MRI scans.
Neil Birkbeck - One of the best experts on this subject based on the ideXlab platform.
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3D Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc. IEEE 11th International Conference on Computer Vision ICCV 2007, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming task performed manually by medical experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue, among different patients and, in many cases, similarity between tumor and normal tissue. One other challenge is how to make use of prior information about the appearance of normal brain. In this paper we propose a variational brain tumor segmentation algorithm that extends current approaches from texture segmentation by using a high dimensional feature set calculated from MRI data and registered atlases. Using manually segmented data we learn a statistical Model for tumor and normal tissue. We show that using a Conditional Model to discriminate between normal and abnormal regions significantly improves the segmentation results compared to traditional generative Models. Validation is performed by testing the method on several cancer patient MRI scans.
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{3D} Variational Brain Tumor Segmentation using a High Dimensional Feature Set
Proc ICCV, 2007Co-Authors: Dana Cobzaş, Martin Jägersand, Neil Birkbeck, Mark Schmidt, A MurthaAbstract:Tumor segmentation from MRI data is an important but time consuming\ntask performed manually by medical experts. Automating this process\nis challenging due to the high diversity in appearance of tumor tissue,\namong different patients and, in many cases, similarity between tumor\nand normal tissue. One other challenge is how to make use of prior\ninformation about the appearance of normal brain. In this paper we\npropose a variational brain tumor segmentation algorithm that extends\ncurrent approaches from texture segmentation by using a high dimensional\nfeature set calculated from MRI data and registered atlases. Using\nmanually segmented data we learn a statistical Model for tumor and\nnormal tissue. We show that using a Conditional Model to discriminate\nbetween normal and abnormal regions significantly improves the segmentation\nresults compared to traditional generative Models. Validation is\nperformed by testing the method on several cancer patient MRI scans.