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441 results on '"Vaillant G"'

9. Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

Author

Bai, W, Sinclair, M, Tarroni, G, Oktay, O, Rajchl, M, Vaillant, G, Lee, AM, Aung, N, Lukaschuk, E, Sanghvi, MM, Zemrak, F, Fung, K, Paiva, JM, Carapella, V, Kim, YJ, Suzuki, H, Kainz, B, Matthews, PM, Petersen, SE, Piechnik, SK, Neubauer, S, Glocker, B, Rueckert, D, Engineering & Physical Science Research Council (E, Engineering & Physical Science Research Council (EPSRC), UK DRI Ltd, Medical Research Council (MRC), and Imperial College London

Subjects
FOS: Computer and information sciences, Male, lcsh:Diseases of the circulatory (Cardiovascular) system, LEFT-VENTRICLE, Cardiac & Cardiovascular Systems, Databases, Factual, Heart Diseases, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, SEGMENTATION, Magnetic Resonance Imaging, Cine, DIAGNOSIS, Ventricular Function, Left, Automation, Deep Learning, Predictive Value of Tests, Machine learning, Image Interpretation, Computer-Assisted, LEVEL SET, Humans, Fully convolutional networks, 1102 Cardiorespiratory Medicine and Haematology, cs.CV, 080104 Computer Vision, Aged, Observer Variation, Science & Technology, T1, Research, Radiology, Nuclear Medicine & Medical Imaging, Reproducibility of Results, Stroke Volume, Middle Aged, Myocardial Contraction, Nuclear Medicine & Medical Imaging, CMR image analysis, lcsh:RC666-701, Cardiovascular System & Cardiology, cardiovascular system, Ventricular Function, Right, HEART-FAILURE, Female, 08 Information and Computing Sciences, Neural Networks, Computer, Life Sciences & Biomedicine, RC
Abstract

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a large-scale dataset from the UK Biobank, consisting of 4,875 subjects with 93,500 pixelwise annotated images. The performance of the method has been evaluated using a number of technical metrics, including the Dice metric, mean contour distance and Hausdorff distance, as well as clinically relevant measures, including left ventricle (LV) end-diastolic volume (LVEDV) and end-systolic volume (LVESV), LV mass (LVM); right ventricle (RV) end-diastolic volume (RVEDV) and end-systolic volume (RVESV). By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images., Comment: Accepted for publication by Journal of Cardiovascular Magnetic Resonance

Published
2018

11. Human-level CMR image analysis with deep fully convolutional networks

Author

Bai, W, Sinclair, M, Tarroni, G, Oktay, O, Rajchl, M, Vaillant, G, Lee, AM, Aung, N, Lukaschuk, E, Sanghvi, MM, Zemrak, F, Fung, K, Paiva, JM, Carapella, V, Kim, YJ, Suzuki, H, Kainz, B, Matthews, PM, Petersen, SE, Piechnik, SK, Neubauer, S, Glocker, B, and Rueckert, D

Subjects
cardiovascular system, cardiovascular diseases, cs.CV
Abstract

Cardiovascular magnetic resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing a wealth of information for sensitive and specific diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically relevant information from CMR images. Deep neural networks have shown a great potential in image pattern recognition and segmentation for a variety of tasks. Here we demonstrate an automated analysis method for CMR images, which is based on a fully convolutional network (FCN). The network is trained and evaluated on a dataset of unprecedented size, consisting of 4,875 subjects with 93,500 pixelwise annotated images, which is by far the largest annotated CMR dataset. By combining FCN with a large-scale annotated dataset, we show for the first time that an automated method achieves a performance on par with human experts in analysing CMR images and deriving clinical measures. We anticipate this to be a starting point for automated and comprehensive CMR analysis with human-level performance, facilitated by machine learning. It is an important advance on the pathway towards computer-assisted CVD assessment.

Published
2017

12. Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests

Author

Lorch, B, Vaillant, G, Baumgartner, C, Bai, W, Rueckert, D, and Maier, A

Subjects
Article Subject, Technische Fakultät, ddc:000, sense organs
Abstract

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

Published
2017
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