no code implementations • 18 Feb 2023 • Bo Zhou, Jo Schlemper, Neel Dey, Seyed Sadegh Mohseni Salehi, Kevin Sheth, Chi Liu, James S. Duncan, Michal Sofka
To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.
no code implementations • 27 Jun 2022 • Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi, Bo Zhou, Guido Gerig, Michal Sofka
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task.
no code implementations • 26 Jan 2022 • Bo Zhou, Neel Dey, Jo Schlemper, Seyed Sadegh Mohseni Salehi, Chi Liu, James S. Duncan, Michal Sofka
To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction.
1 code implementation • 22 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.
no code implementations • 12 Jul 2020 • Chen Qin, Jo Schlemper, Kerstin Hammernik, Jinming Duan, Ronald M. Summers, Daniel Rueckert
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
1 code implementation • 18 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.
1 code implementation • 11 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.
no code implementations • 25 Sep 2019 • Jo Schlemper, Jinming Duan, Cheng Ouyang, Chen Qin, Jose Caballero, Joseph V. Hajnal, Daniel Rueckert
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
1 code implementation • 24 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.
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.
no code implementations • 20 Aug 2019 • Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data.
1 code implementation • 22 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.
1 code implementation • 19 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.
no code implementations • 11 Feb 2019 • Jo Schlemper, Jose Caballero, Andy Aitken, Joost van Amersfoort
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals.
no code implementations • 31 Jan 2019 • Cheng Ouyang, Jo Schlemper, Carlo Biffi, Gavin Seegoolam, Jose Caballero, Anthony N. Price, Joseph V. Hajnal, Daniel Rueckert
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
no code implementations • 20 Nov 2018 • Qingjie Meng, Matthew Sinclair, Veronika Zimmer, Benjamin Hou, Martin Rajchl, Nicolas Toussaint, Ozan Oktay, Jo Schlemper, Alberto Gomez, James Housden, Jacqueline Matthew, Daniel Rueckert, Julia Schnabel, Bernhard Kainz
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
1 code implementation • 26 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.
2 code implementations • 22 Aug 2018 • Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert
AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.
no code implementations • 27 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).
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.
1 code implementation • 11 Jun 2018 • Chen Qin, Wenjia Bai, Jo Schlemper, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
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.
6 code implementations • 15 Apr 2018 • Jo Schlemper, Ozan Oktay, Liang Chen, Jacqueline Matthew, Caroline Knight, Bernhard Kainz, Ben Glocker, Daniel Rueckert
We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance.
36 code implementations • 11 Apr 2018 • Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y. Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on
Pancreas Segmentation
on CT-150
4 code implementations • 5 Dec 2017 • Chen Qin, Jo Schlemper, Jose Caballero, Anthony Price, Joseph V. Hajnal, Daniel Rueckert
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
4 code implementations • 8 Apr 2017 • Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
4 code implementations • 1 Mar 2017 • Jo Schlemper, Jose Caballero, Joseph V. Hajnal, Anthony Price, Daniel Rueckert
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.