1 code implementation • 17 Sep 2021 • Jun Chen, Heye Zhang, Raad Mohiaddin, Tom Wong, David Firmin, Jennifer Keegan, Guang Yang
For the inter-domain learning, a consistency constraint is applied to the LAs modelled by two dual-modelling networks to exploit the complementary knowledge among different data domains.
no code implementations • 1 May 2021 • Jun Chen, Guang Yang, Habib Khan, Heye Zhang, Yanping Zhang, Shu Zhao, Raad Mohiaddin, Tom Wong, David Firmin, Jennifer Keegan
In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way.
no code implementations • 26 Apr 2021 • Mengmeng Kuang, Yinzhe Wu, Diego Alonso-Álvarez, David Firmin, Jennifer Keegan, Peter Gatehouse, Guang Yang
Myocardial Velocity Mapping Cardiac MR (MVM-CMR) can be used to measure global and regional myocardial velocities with proved reproducibility.
no code implementations • 16 Dec 2020 • Yinzhe Wu, Suzan Hatipoglu, Diego Alonso-Álvarez, Peter Gatehouse, David Firmin, Jennifer Keegan, Guang Yang
Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.
no code implementations • 2 Feb 2020 • Guang Yang, Jun Chen, Zhifan Gao, Shuo Li, Hao Ni, Elsa Angelini, Tom Wong, Raad Mohiaddin, Eva Nyktari, Ricardo Wage, Lei Xu, Yanping Zhang, Xiuquan Du, Heye Zhang, David Firmin, Jennifer Keegan
Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0. 27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices).
no code implementations • 24 Jul 2019 • Jun Chen, Heye Zhang, Yanping Zhang, Shu Zhao, Raad Mohiaddin, Tom Wong, David Firmin, Guang Yang, Jennifer Keegan
Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation.
no code implementations • 21 Feb 2019 • Lei Li, Fuping Wu, Guang Yang, Lingchao Xu, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Xiahai Zhuang
Compared with the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0. 01).
no code implementations • 21 Feb 2019 • Xiahai Zhuang, Lei LI, Christian Payer, Darko Stern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Orjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Thierry Brouard, Qianqian Tong, Weixin Si, Xiangyun Liao, Guodong Zeng, Zenglin Shi, Guoyan Zheng, Chengjia Wang, Tom MacGillivray, David Newby, Kawal Rhode, Sebastien Ourselin, Raad Mohiaddin, Jennifer Keegan, David Firmin, Guang Yang
This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017.
no code implementations • 22 Oct 2018 • Fuping Wu, Lei LI, Guang Yang, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Lingchao Xu, Xiahai Zhuang
We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session.
no code implementations • 20 Aug 2018 • Christina Koutsoumpa, Jennifer Keegan, David Firmin, Guang-Zhong Yang, Duncan Gillies
Methods: Translational motion is estimated from a suitable region of velocities automatically defined in the left-ventricular volume.
1 code implementation • 28 Jun 2018 • Maximilian Seitzer, Guang Yang, Jo Schlemper, Ozan Oktay, Tobias Würfl, Vincent Christlein, Tom Wong, Raad Mohiaddin, David Firmin, Jennifer Keegan, Daniel Rueckert, Andreas Maier
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
no code implementations • 12 Jun 2018 • Jun Chen, Guang Yang, Zhifan Gao, Hao Ni, Elsa Angelini, Raad Mohiaddin, Tom Wong, Yanping Zhang, Xiuquan Du, Heye Zhang, Jennifer Keegan, David Firmin
Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success.
no code implementations • 30 May 2018 • Jo Schlemper, Guang Yang, Pedro Ferreira, Andrew Scott, Laura-Ann McGill, Zohya Khalique, Margarita Gorodezky, Malte Roehl, Jennifer Keegan, Dudley Pennell, David Firmin, Daniel Rueckert
To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction.
no code implementations • 26 May 2017 • Guang Yang, Xiahai Zhuang, Habib Khan, Shouvik Haldar, Eva Nyktari, Lei LI, Rick Wage, Xujiong Ye, Greg Slabaugh, Raad Mohiaddin, Tom Wong, Jennifer Keegan, David Firmin
In this study, we proposed a novel fully automatic pipeline to achieve an accurate and objective atrial scarring segmentation and assessment of LGE MRI scans for the AF patients.
no code implementations • 19 May 2017 • Simiao Yu, Hao Dong, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinical applications in order to reduce the scanning cost and improve the patient experience.