Zobrazeno 1 - 10
of 28
pro vyhledávání: '"de Marvao A"'
Autor:
Qiao, Mengyun, Wang, Shuo, Qiu, Huaqi, de Marvao, Antonio, O'Regan, Declan P., Rueckert, Daniel, Bai, Wenjia
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can ofte
Externí odkaz:
http://arxiv.org/abs/2301.13098
Autor:
Meng, Qingjie, Qin, Chen, Bai, Wenjia, Liu, Tianrui, de Marvao, Antonio, O'Regan, Declan P, Rueckert, Daniel
Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estima
Externí odkaz:
http://arxiv.org/abs/2208.00034
Autor:
Thorley, Alexander, Jia, Xi, Chang, Hyung Jin, Liu, Boyang, Bunting, Karina, Stoll, Victoria, de Marvao, Antonio, O'Regan, Declan P., Gkoutos, Georgios, Kotecha, Dipak, Duan, Jinming
Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate, they typically carry a significant computational burden. Recent devel
Externí odkaz:
http://arxiv.org/abs/2109.12688
Autor:
Savioli, Nicolo, de Marvao, Antonio, Bai, Wenjia, Wang, Shuo, Cook, Stuart A., Chin, Calvin W. L., Rueckert, Daniel, O'Regan, Declan P.
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion. However, techniques such as cardiac magnetic resonance imaging are conventionally limited to acquiring contiguous cross-sectional slic
Externí odkaz:
http://arxiv.org/abs/2107.07975
Autor:
Jia, Xi, Thorley, Alexander, Chen, Wei, Qiu, Huaqi, Shen, Linlin, Styles, Iain B, Chang, Hyung Jin, Leonardis, Ales, de Marvao, Antonio, O'Regan, Declan P., Rueckert, Daniel, Duan, Jinming
Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this whilst retaining the fast inference speed of deep learning, we propose V
Externí odkaz:
http://arxiv.org/abs/2105.12227
Autor:
Biffi, Carlo, Cerrolaza, Juan J., Tarroni, Giacomo, Bai, Wenjia, de Marvao, Antonio, Oktay, Ozan, Ledig, Christian, Folgoc, Loic Le, Kamnitsas, Konstantinos, Doumou, Georgia, Duan, Jinming, Prasad, Sanjay K., Cook, Stuart A., O'Regan, Declan P., Rueckert, Daniel
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagno
Externí odkaz:
http://arxiv.org/abs/1907.00058
Autor:
Biffi, Carlo, Cerrolaza, Juan J., Tarroni, Giacomo, de Marvao, Antonio, Cook, Stuart A., O'Regan, Declan P., Rueckert, Daniel
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often requir
Externí odkaz:
http://arxiv.org/abs/1902.11000
Autor:
Bello, Ghalib A., Dawes, Timothy J. W., Duan, Jinming, Biffi, Carlo, de Marvao, Antonio, Howard, Luke S. G. E., Gibbs, J. Simon R., Wilkins, Martin R., Cook, Stuart A., Rueckert, Daniel, O'Regan, Declan P.
Publikováno v:
Nature Machine Intelligence, 1, 95-104 (2019)
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representa
Externí odkaz:
http://arxiv.org/abs/1810.03382
Autor:
Tarroni, Giacomo, Oktay, Ozan, Sinclair, Matthew, Bai, Wenjia, Schuh, Andreas, Suzuki, Hideaki, de Marvao, Antonio, O'Regan, Declan, Cook, Stuart, Rueckert, Daniel
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by in
Externí odkaz:
http://arxiv.org/abs/1810.02201
Autor:
Duan, Jinming, Bello, Ghalib, Schlemper, Jo, Bai, Wenjia, Dawes, Timothy J W, Biffi, Carlo, de Marvao, Antonio, Doumou, Georgia, O'Regan, Declan P, Rueckert, Daniel
Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anat
Externí odkaz:
http://arxiv.org/abs/1808.08578