no code implementations • 17 Dec 2024 • Vasiliki Sideri-Lampretsa, Nil Stolt-Ansó, Martin Menten, Huaqi Qiu, Julian McGinnis, Daniel Rueckert
We employ this formulation to construct a multi-resolution deformable registration model, where deformation layers iteratively refine the overall transformation at each resolution without intermediate resampling operations on the feature grids.
no code implementations • 6 Sep 2023 • Vasiliki Sideri-Lampretsa, Veronika A. Zimmer, Huaqi Qiu, Georgios Kaissis, Daniel Rueckert
The success of multi-modal image registration, whether it is conventional or learning based, is predicated upon the choice of an appropriate distance (or similarity) measure.
no code implementations • 19 Mar 2023 • Yinsong Wang, Huaqi Qiu, Chen Qin
The proposed method introduces a spatially-variant regularization and learns its effect of achieving spatially-adaptive regularization by conditioning the registration network on the hyperparameter matrix via CSAIN.
1 code implementation • 30 Jan 2023 • Mengyun Qiao, Shuo Wang, Huaqi Qiu, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Wenjia Bai
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.
1 code implementation • 28 Aug 2022 • Mengyun Qiao, Berke Doga Basaran, Huaqi Qiu, Shuo Wang, Yi Guo, Yuanyuan Wang, Paul M. Matthews, Daniel Rueckert, Wenjia Bai
Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression.
no code implementations • 8 Dec 2021 • Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
1 code implementation • 7 Dec 2021 • Huaqi Qiu, Kerstin Hammernik, Chen Qin, Chen Chen, Daniel Rueckert
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass.
1 code implementation • 25 Oct 2021 • Daniel Grzech, Mohammad Farid Azampour, Huaqi Qiu, Ben Glocker, Bernhard Kainz, Loïc le Folgoc
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification.
no code implementations • 8 Oct 2021 • Loic Le Folgoc, Vasileios Baltatzis, Sujal Desai, Anand Devaraj, Sam Ellis, Octavio E. Martinez Manzanera, Arjun Nair, Huaqi Qiu, Julia Schnabel, Ben Glocker
We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact.
1 code implementation • 7 Aug 2021 • Chen Chen, Chen Qin, Cheng Ouyang, Zeju Li, Shuo Wang, Huaqi Qiu, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
no code implementations • 25 May 2021 • Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B Styles, Hyung Jin Chang, Ales Leonardis, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert, Jinming Duan
We then propose two neural layers (i. e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net to formulate the denoising problem (i. e. generalized denoising layer).
1 code implementation • 9 Nov 2020 • Jeremy Tan, Benjamin Hou, James Batten, Huaqi Qiu, Bernhard Kainz
A wide residual encoder decoder is trained to give a pixel-wise prediction of the patch and its interpolation factor.
4 code implementations • ECCV 2020 • Cheng Ouyang, Carlo Biffi, Chen Chen, Turkay Kart, Huaqi Qiu, Daniel Rueckert
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
1 code implementation • 23 Jun 2020 • Chen Chen, Chen Qin, Huaqi Qiu, Cheng Ouyang, Shuo Wang, Liang Chen, Giacomo Tarroni, Wenjia Bai, Daniel Rueckert
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.
1 code implementation • 8 Jun 2020 • Chen Qin, Shuo Wang, Chen Chen, Huaqi Qiu, Wenjia Bai, Daniel Rueckert
The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.
no code implementations • 9 Nov 2019 • Chen Chen, Chen Qin, Huaqi Qiu, Giacomo Tarroni, Jinming Duan, Wenjia Bai, Daniel Rueckert
Deep learning has become the most widely used approach for cardiac image segmentation in recent years.
no code implementations • 20 Aug 2019 • Chen Chen, Cheng Ouyang, Giacomo Tarroni, Jo Schlemper, Huaqi Qiu, Wenjia Bai, Daniel Rueckert
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.