Browsing NIHR Advanced Imaging by Author "Bai, W"
Now showing items 1-12 of 12
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Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: A 2-center study
Malcolme-Lawes, LC; Juli, C; Karim, R; Bai, W; Quest, R; Lim, PB; Jamil-Copley, S; Kojodjojo, P; Ariff, B; Davies, DW (2013-08) -
Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.
Bai, W; Sinclair, M; Tarroni, G; Oktay, O; Rajchl, M; Vaillant, G; Lee, AM; Aung, N; Lukaschuk, E; Sanghvi, MM (2018-09-14)BACKGROUND: Cardiovascular 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 ... -
Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study
PETERSEN, SE; Robinson, R; Valindria, V; Bai, W; Oktay, O; Kainz, B; Suzuki, H; Sanghvi, M; Aung, N; Paiva, J (BioMed Central, 2019-03-14)Background: The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods ... -
Fully automated left ventricular analysis matches clinician precision: a multi-centre, multi-vendor, multi-field strength, multi-disease scan:rescan CMR study
Bhuva, A; Bai, W; Lau, C; Davies, R; Yang, Y; Bulluck, H; Mcalindon, E; Cole, GD; Petersen, SE; Greenwood, JP (2019-06) -
Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images
Chen, C; Bai, W; Davies, RH; Bhuva, AN; Manisty, CH; Augusto, JB; Moon, JC; Aung, N; Lee, AM; Sanghvi, MM (2020-06-30) -
Independent Left Ventricular Morphometric Atlases Show Consistent Relationships with Cardiovascular Risk Factors: A UK Biobank Study
PETERSEN, SE; Gilbert, K; Bai, W; Mauger, C; Medrano-Gracia, P; Suinesiaputra, A; Lee, AM; Sanghvi, MM; Aung, N; Piechnik, SK (Nature Publishing Group, 2019-02-04)Left ventricular (LV) mass and volume are important indicators of clinical and pre-clinical disease processes. However, much of the shape information present in modern imaging examinations is currently ignored. Morphometric ... -
Joint learning of motion estimation and segmentation for cardiac MR image sequences
Qin, C; Bai, W; Schlemper, J; Petersen, SE; Piechnik, SK; Neubauer, S; Rueckert, D (2018-01-01)© Springer Nature Switzerland AG 2018. Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases. In this paper, we propose a novel ... -
Joint motion estimation and segmentation from undersampled cardiac mr image
Qin, C; Bai, W; Schlemper, J; Petersen, SE; Piechnik, SK; Neubauer, S; Rueckert, D (2018-01-01)© 2018, Springer Nature Switzerland AG. Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data. ... -
Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images
Chen, C; Biffi, C; Tarroni, G; Petersen, S; Bai, W; Rueckert, D (2019-01-01)© 2019, Springer Nature Switzerland AG. Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function ... -
A Multicenter, Scan-Rescan, Human and Machine Learning CMR Study to Test Generalizability and Precision in Imaging Biomarker Analysis.
Bhuva, A; Bai, W; Lau, C; Davies, R; Ye, Y; Bulluck, H; McAlindon, E; Culotta, V; Swoboda, P; Captur, G (2019-10)BACKGROUND: Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a ... -
A population-based phenome-wide association study of cardiac and aortic structure and function
Bai, W; Suzuki, H; Huang, J; Francis, C; Wang, S; Tarroni, G; Guitton, F; Aung, N; Fung, K; Petersen, SE (2020-10) -
Real-Time Prediction of Segmentation Quality
Robinson, R; Oktay, O; Bai, W; Valindria, VV; Sanghvi, MM; Aung, N; Paiva, JM; Zemrak, F; Fung, K; Lukaschuk, E (2018-01-01)© 2018, Springer Nature Switzerland AG. Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due ...