Zobrazeno 1 - 10
of 229
pro vyhledávání: '"Taylor, Zeike"'
Autor:
Wijesinghe, Isuru, Nix, Michael, Zakeri, Arezoo, Hokmabadi, Alireza, Al-Qaisieh, Bashar, Gooya, Ali, Taylor, Zeike A.
We propose Deep-Motion-Net: an end-to-end graph neural network (GNN) architecture that enables 3D (volumetric) organ shape reconstruction from a single in-treatment kV planar X-ray image acquired at any arbitrary projection angle. Estimating and comp
Externí odkaz:
http://arxiv.org/abs/2407.06692
Autor:
Min, Zhe, Baum, Zachary M. C., Saeed, Shaheer U., Emberton, Mark, Barratt, Dean C., Taylor, Zeike A., Hu, Yipeng
This paper investigates both biomechanical-constrained non-rigid medical image registrations and accurate identifications of material properties for soft tissues, using physics-informed neural networks (PINNs). The complex nonlinear elasticity theory
Externí odkaz:
http://arxiv.org/abs/2407.03292
Autor:
Gaggion, Nicolás, Matheson, Benjamin A., Xia, Yan, Bonazzola, Rodrigo, Ravikumar, Nishant, Taylor, Zeike A., Milone, Diego H., Frangi, Alejandro F., Ferrante, Enzo
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational an
Externí odkaz:
http://arxiv.org/abs/2311.13706
Autor:
Min, Zhe, Baum, Zachary M. C., Saeed, Shaheer U., Emberton, Mark, Barratt, Dean C., Taylor, Zeike A., Hu, Yipeng
Biomechanical modelling of soft tissue provides a non-data-driven method for constraining medical image registration, such that the estimated spatial transformation is considered biophysically plausible. This has not only been adopted in real-world c
Externí odkaz:
http://arxiv.org/abs/2302.10343
Autor:
Zakeri, Arezoo, Hokmabadi, Alireza, Nix, Michael G., Gooya, Ali, Wijesinghe, Isuru, Taylor, Zeike A.
Publikováno v:
In Computer Methods and Programs in Biomedicine June 2024 250
Autor:
Saeed, Shaheer U., Taylor, Zeike A., Pinnock, Mark A., Emberton, Mark, Barratt, Dean C., Hu, Yipeng
In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operati
Externí odkaz:
http://arxiv.org/abs/2007.04972
Akademický článek
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Akademický článek
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Autor:
Zakeri, Arezoo, Hokmabadi, Alireza, Bi, Ning, Wijesinghe, Isuru, Nix, Michael G., Petersen, Steffen E., Frangi, Alejandro F., Taylor, Zeike A., Gooya, Ali
Publikováno v:
In Medical Image Analysis January 2023 83
Publikováno v:
In Computerized Medical Imaging and Graphics December 2021 94