Search Results for author: Mark Emberton

Found 6 papers, 0 papers with code

Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes

no code implementations9 Jul 2020 Shaheer U. Saeed, Zeike A. Taylor, Mark A. Pinnock, Mark Emberton, Dean C. Barratt, Yipeng Hu

Based on 160, 000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0. 017 mm in predicted nodal displacement.

Conditional Segmentation in Lieu of Image Registration

no code implementations30 Jun 2019 Yipeng Hu, Eli Gibson, Dean C. Barratt, Mark Emberton, J. Alison Noble, Tom Vercauteren

Classical pairwise image registration methods search for a spatial transformation that optimises a numerical measure that indicates how well a pair of moving and fixed images are aligned.

Image Registration Semantic Segmentation

Weakly-Supervised Convolutional Neural Networks for Multimodal Image Registration

no code implementations9 Jul 2018 Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M. Moore, Mark Emberton, Sébastien Ourselin, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

A median target registration error of 3. 6 mm on landmark centroids and a median Dice of 0. 87 on prostate glands are achieved from cross-validation experiments, in which 108 pairs of multimodal images from 76 patients were tested with high-quality anatomical labels.

Image Registration

Adversarial Deformation Regularization for Training Image Registration Neural Networks

no code implementations27 May 2018 Yipeng Hu, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, Tom Vercauteren, J. Alison Noble, Dean C. Barratt

During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation.

Image Registration

Label-driven weakly-supervised learning for multimodal deformable image registration

no code implementations5 Nov 2017 Yipeng Hu, Marc Modat, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, J. Alison Noble, Dean C. Barratt, Tom Vercauteren

Spatially aligning medical images from different modalities remains a challenging task, especially for intraoperative applications that require fast and robust algorithms.

Image Registration

Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

no code implementations5 Sep 2017 Yipeng Hu, Eli Gibson, Tom Vercauteren, Hashim U. Ahmed, Mark Emberton, Caroline M. Moore, J. Alison Noble, Dean C. Barratt

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image.

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