The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Konstantinos Kamnitsas - One of the best experts on this subject based on the ideXlab platform.
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unsupervised domain adaptation in Brain Lesion segmentation with adversarial networks
International Conference Information Processing, 2017Co-Authors: Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, Konstantinos Kamnitsas, David K Menon, Christian Ledig, Christian F Baumgartner, Aditya V Nori, Antonio CriminisiAbstract:Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more robust to differences in the input data, and which does not require any annotations on the test domain. Specifically, we derive domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic Brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
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efficient multi scale 3d cnn with fully connected crf for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute Brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.
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Efficient multi-scale 3D CNN with fully connected CRF for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of Brain Lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of Lesion segmentation in multi-channel MRI patient data with traumatic Brain injuries, Brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
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unsupervised domain adaptation in Brain Lesion segmentation with adversarial networks
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, Konstantinos Kamnitsas, David K Menon, Christian Ledig, Christian F Baumgartner, Aditya V Nori, Antonio CriminisiAbstract:Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic Brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
Chuyang Ye - One of the best experts on this subject based on the ideXlab platform.
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Semi-Supervised Brain Lesion Segmentation Using Training Images with and Without Lesions
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020Co-Authors: Fengqian Pang, Kongming Liang, Xiuli Li, Xiangzhu Zeng, Chuyang YeAbstract:Semi-supervised approaches have been developed to improve Brain Lesion segmentation based on convolutional neural networks (CNNs) when annotated data is scarce. Existing methods have exploited unannotated images with Lesions to improve the training of CNNs. In this work, we explore semi-supervised Brain Lesion segmentation by further incorporating images without Lesions. Specifically, using information learned from annotated and unannotated scans with Lesions, we propose a framework to generate synthesized Lesions and their annotations simultaneously. Then, we attach them to normal-appearing scans using a statistical model to produce synthesized training samples, which are used together with true annotations to train CNNs for segmentation. Experimental results show that our method outperforms competing semi-supervised Brain Lesion segmentation approaches.
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semi supervised Brain Lesion segmentation with an adapted mean teacher model
International Conference Information Processing, 2019Co-Authors: Yuxing Li, Xiuli Li, Xiangzhu Zeng, Yiming Li, Tianle Wang, Chuyang YeAbstract:Automated Brain Lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods that are based on convolutional neural networks (CNNs) have achieved state-of-the-art segmentation performance. However, CNNs usually require a decent amount of annotated data, which may be costly and time-consuming to obtain. Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available. In this work, we propose a semi-supervised learning (SSL) approach to Brain Lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher model, which is originally developed for SSL-based image classification, for Brain Lesion segmentation. Assuming that the network should produce consistent outputs for similar inputs, a loss of segmentation consistency is designed and integrated into a self-ensembling framework. Self-ensembling exploits the information in the intermediate training steps, and the ensemble prediction based on the information can be closer to the correct result than the single latest model. To exploit such information, we build a student model and a teacher model, which share the same CNN architecture for segmentation. The student and teacher models are updated alternately. At each step, the student model learns from the teacher model by minimizing the weighted sum of the segmentation loss computed from annotated data and the segmentation consistency loss between the teacher and student models computed from unannotated data. Then, the teacher model is updated by combining the updated student model with the historical information of teacher models using an exponential moving average strategy. For demonstration, the proposed approach was evaluated on ischemic stroke Lesion segmentation. Results indicate that the proposed method improves stroke Lesion segmentation with the incorporation of unannotated data and outperforms competing SSL-based methods.
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semi supervised Brain Lesion segmentation with an adapted mean teacher model
arXiv: Computer Vision and Pattern Recognition, 2019Co-Authors: Yuxing Li, Xiuli Li, Xiangzhu Zeng, Yiming Li, Tianle Wang, Chuyang YeAbstract:Automated Brain Lesion segmentation provides valuable information for the analysis and intervention of patients. In particular, methods based on convolutional neural networks (CNNs) have achieved state-of-the-art segmentation performance. However, CNNs usually require a decent amount of annotated data, which may be costly and time-consuming to obtain. Since unannotated data is generally abundant, it is desirable to use unannotated data to improve the segmentation performance for CNNs when limited annotated data is available. In this work, we propose a semi-supervised learning (SSL) approach to Brain Lesion segmentation, where unannotated data is incorporated into the training of CNNs. We adapt the mean teacher model, which is originally developed for SSL-based image classification, for Brain Lesion segmentation. Assuming that the network should produce consistent outputs for similar inputs, a loss of segmentation consistency is designed and integrated into a self-ensembling framework. Specifically, we build a student model and a teacher model, which share the same CNN architecture for segmentation. The student and teacher models are updated alternately. At each step, the student model learns from the teacher model by minimizing the weighted sum of the segmentation loss computed from annotated data and the segmentation consistency loss between the teacher and student models computed from unannotated data. Then, the teacher model is updated by combining the updated student model with the historical information of teacher models using an exponential moving average strategy. For demonstration, the proposed approach was evaluated on ischemic stroke Lesion segmentation, where it improves stroke Lesion segmentation with the incorporation of unannotated data.
Bernd Glocker - One of the best experts on this subject based on the ideXlab platform.
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efficient multi scale 3d cnn with fully connected crf for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute Brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.
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Efficient multi-scale 3D CNN with fully connected CRF for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of Brain Lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of Lesion segmentation in multi-channel MRI patient data with traumatic Brain injuries, Brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
Daniel Rueckert - One of the best experts on this subject based on the ideXlab platform.
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efficient multi scale 3d cnn with fully connected crf for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute Brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.
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Efficient multi-scale 3D CNN with fully connected CRF for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of Brain Lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of Lesion segmentation in multi-channel MRI patient data with traumatic Brain injuries, Brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
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Brain Lesion segmentation through image synthesis and outlier detection
NeuroImage: Clinical, 2017Co-Authors: Christopher T Bowles, Maria Del C Valdes Hernandez, Ricardo Guerrero, Roger N Gunn, Alexander Hammers, David Alexander Dickie, Joanna M Wardlaw, Daniel RueckertAbstract:Cerebral small vessel disease (SVD) can manifest in a number of ways. Many of these result in hyperintense regions visible on T2-weighted magnetic resonance (MR) images. The automatic segmentation of these Lesions has been the focus of many studies. However, previous methods tended to be limited to certain types of pathology, as a consequence of either restricting the search to the white matter, or by training on an individual pathology. Here we present an unsupervised abnormality detection method which is able to detect abnormally hyperintense regions on FLAIR regardless of the underlying pathology or location. The method uses a combination of image synthesis, Gaussian mixture models and one class support vector machines, and needs only be trained on healthy tissue. We evaluate our method by comparing segmentation results from 127 subjects with SVD with three established methods and report significantly superior performance across a number of metrics.
Virginia F.j. Newcombe - One of the best experts on this subject based on the ideXlab platform.
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unsupervised domain adaptation in Brain Lesion segmentation with adversarial networks
International Conference Information Processing, 2017Co-Authors: Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, Konstantinos Kamnitsas, David K Menon, Christian Ledig, Christian F Baumgartner, Aditya V Nori, Antonio CriminisiAbstract:Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more robust to differences in the input data, and which does not require any annotations on the test domain. Specifically, we derive domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic Brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
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efficient multi scale 3d cnn with fully connected crf for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:This work is supported by the EPSRC First Grant scheme (grant ref no. EP/N023668/1) and partially funded under the 7th Framework Programme by the European Commission (TBIcare: http: //www.tbicare.eu/ ; CENTER-TBI: https://www.center-tbi.eu/). This work was further supported by a Medical Research Council (UK) Program Grant (Acute Brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [G9439390 ID 65883]), the UK National Institute of Health Research Biomedical Research Centre at Cambridge and Technology Platform funding provided by the UK Department of Health. KK is supported by the Imperial College London PhD Scholarship Programme. VFJN is supported by a Health Foundation/Academy of Medical Sciences Clinician Scientist Fellowship. DKM is supported by an NIHR Senior Investigator Award. We gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs for our research.
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Efficient multi-scale 3D CNN with fully connected CRF for accurate Brain Lesion segmentation
Medical Image Analysis, 2017Co-Authors: Konstantinos Kamnitsas, Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K Menon, Christian Ledig, Daniel Rueckert, Bernd GlockerAbstract:We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of Brain Lesion segmentation. The devised architecture is the result of an in-depth analysis of the limitations of current networks proposed for similar applications. To overcome the computational burden of processing 3D medical scans, we have devised an efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data. Further, we analyze the development of deeper, thus more discriminative 3D CNNs. In order to incorporate both local and larger contextual information, we employ a dual pathway architecture that processes the input images at multiple scales simultaneously. For post-processing of the network's soft segmentation, we use a 3D fully connected Conditional Random Field which effectively removes false positives. Our pipeline is extensively evaluated on three challenging tasks of Lesion segmentation in multi-channel MRI patient data with traumatic Brain injuries, Brain tumours, and ischemic stroke. We improve on the state-of-the-art for all three applications, with top ranking performance on the public benchmarks BRATS 2015 and ISLES 2015. Our method is computationally efficient, which allows its adoption in a variety of research and clinical settings. The source code of our implementation is made publicly available.
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unsupervised domain adaptation in Brain Lesion segmentation with adversarial networks
arXiv: Computer Vision and Pattern Recognition, 2016Co-Authors: Virginia F.j. Newcombe, Joanna P. Simpson, Andrew D. Kane, Konstantinos Kamnitsas, David K Menon, Christian Ledig, Christian F Baumgartner, Aditya V Nori, Antonio CriminisiAbstract:Significant advances have been made towards building accurate automatic segmentation systems for a variety of biomedical applications using machine learning. However, the performance of these systems often degrades when they are applied on new data that differ from the training data, for example, due to variations in imaging protocols. Manually annotating new data for each test domain is not a feasible solution. In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain. Specifically, we learn domain-invariant features by learning to counter an adversarial network, which attempts to classify the domain of the input data by observing the activations of the segmentation network. Furthermore, we propose a multi-connected domain discriminator for improved adversarial training. Our system is evaluated using two MR databases of subjects with traumatic Brain injuries, acquired using different scanners and imaging protocols. Using our unsupervised approach, we obtain segmentation accuracies which are close to the upper bound of supervised domain adaptation.
