Search Results for author: Jinming Duan

Found 26 papers, 13 papers with code

Accelerated First Order Methods for Variational Imaging

1 code implementation6 Oct 2021 Joseph Bartlett, Jinming Duan

To overcome these drawbacks, we propose a novel adaption to Total Generalised Variation (TGV) regularisation called Total Smooth Variation (TSV), which retains edges and meanwhile does not produce results which contain staircase artefacts.

Denoising MRI Reconstruction +1

Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration

no code implementations26 Sep 2021 Alexander Thorley, Xi Jia, Hyung Jin Chang, Boyang Liu, Karina Bunting, Victoria Stoll, Antonio de Marvao, Declan P. O'Regan, Georgios Gkoutos, Dipak Kotecha, Jinming Duan

Recent developments in stochastic approaches based on deep learning have achieved sub-second runtimes for DiffIR with competitive registration accuracy, offering a fast alternative to conventional iterative methods.

Image Registration

Learning a Model-Driven Variational Network for Deformable Image Registration

no code implementations25 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).

Denoising Image Registration

Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction

1 code implementation22 Dec 2020 Chen Qin, Jinming Duan, Kerstin Hammernik, Jo Schlemper, Thomas Küstner, René Botnar, Claudia Prieto, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert

The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.

De-aliasing Image Reconstruction

Geometry Constrained Weakly Supervised Object Localization

1 code implementation ECCV 2020 Weizeng Lu, Xi Jia, Weicheng Xie, Linlin Shen, Yicong Zhou, Jinming Duan

The detector predicts the object location defined by a set of coefficients describing a geometric shape (i. e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator.

Weakly-Supervised Object Localization

G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features

1 code implementation CVPR 2020 Wei Chen, Xi Jia, Hyung Jin Chang, Jinming Duan, Ales Leonardis

Third, via the predicted segmentation and translation, we transfer the fine object point cloud into a local canonical coordinate, in which we train a rotation localization network to estimate initial object rotation.

6D Pose Estimation 6D Pose Estimation using RGB +1

$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction

1 code implementation18 Dec 2019 Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Daniel Rueckert

Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.

Fine-tuning Image Enhancement +2

$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

1 code implementation11 Dec 2019 Jo Schlemper, Chen Qin, Jinming Duan, Ronald M. Summers, Kerstin Hammernik

We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.

Image Reconstruction SSIM

dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance

1 code implementation24 Sep 2019 Jo Schlemper, Ilkay Oksuz, James R. Clough, Jinming Duan, Andrew P. King, Julia A. Schnabel, Joseph V. Hajnal, Daniel Rueckert

AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.

k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations

1 code implementation22 Jul 2019 Chen Qin, Jo Schlemper, Jinming Duan, Gavin Seegoolam, Anthony Price, Joseph Hajnal, Daniel Rueckert

Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.

Image Reconstruction

VS-Net: Variable splitting network for accelerated parallel MRI reconstruction

1 code implementation19 Jul 2019 Jinming Duan, Jo Schlemper, Chen Qin, Cheng Ouyang, Wenjia Bai, Carlo Biffi, Ghalib Bello, Ben Statton, Declan P. O'Regan, Daniel Rueckert

In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.

MRI Reconstruction

Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

no code implementations5 Jul 2019 Wenjia Bai, Chen Chen, Giacomo Tarroni, Jinming Duan, Florian Guitton, Steffen E. Petersen, Yike Guo, Paul M. Matthews, Daniel Rueckert

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks.

Self-Supervised Learning Semantic Segmentation +1

Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation

no code implementations5 Jul 2019 Cheng Ouyang, Konstantinos Kamnitsas, Carlo Biffi, Jinming Duan, Daniel Rueckert

Deep unsupervised domain adaptation (UDA) aims to improve the performance of a deep neural network model on a target domain, using solely unlabelled target domain data and labelled source domain data.

Medical Image Segmentation Unsupervised Domain Adaptation

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

1 code implementation28 Jun 2019 Carlo Biffi, Juan J. Cerrolaza, Giacomo Tarroni, Wenjia Bai, Antonio de Marvao, Ozan Oktay, Christian Ledig, Loic Le Folgoc, Konstantinos Kamnitsas, Georgia Doumou, Jinming Duan, Sanjay K. Prasad, Stuart A. Cook, Declan P. O'Regan, Daniel Rueckert

At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space.

A new nonlocal forward model for diffuse optical tomography

no code implementations3 Jun 2019 Wenqi Lu, Jinming Duan, Joshua Deepak Veesa, Iain B. Styles

The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium.

Image Reconstruction

Graph- and finite element-based total variation models for the inverse problem in diffuse optical tomography

no code implementations7 Jan 2019 Wenqi Lu, Jinming Duan, David Orive-Miguel, Lionel Herve, Iain B. Styles

Total variation (TV) is a powerful regularization method that has been widely applied in different imaging applications, but is difficult to apply to diffuse optical tomography (DOT) image reconstruction (inverse problem) due to complex and unstructured geometries, non-linearity of the data fitting and regularization terms, and non-differentiability of the regularization term.

Image Reconstruction

Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

1 code implementation26 Aug 2018 Jinming Duan, Ghalib Bello, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Carlo Biffi, Antonio de Marvao, Georgia Doumou, Declan P. O'Regan, Daniel Rueckert

The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.

Semantic Segmentation

Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension

no code implementations27 Jul 2018 Jinming Duan, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Ghalib Bello, Georgia Doumou, Antonio de Marvao, Declan P. O'Regan, Daniel Rueckert

In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH).

Tensor Based Second Order Variational Model for Image Reconstruction

no code implementations27 Sep 2016 Jinming Duan, Wil OC Ward, Luke Sibbett, Zhenkuan Pan, Li Bai

Second order total variation (SOTV) models have advantages for image reconstruction over their first order counterparts including their ability to remove the staircase artefact in the reconstructed image, but they tend to blur the reconstructed image.

BSDS500 Denoising +2

Automated Segmentation of Retinal Layers from Optical Coherent Tomography Images Using Geodesic Distance

no code implementations7 Sep 2016 Jinming Duan, Christopher Tench, Irene Gottlob, Frank Proudlock, Li Bai

OCT image segmentation to localise retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve disorders.

Semantic Segmentation

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