Search Results for author: Daniel Rueckert

Found 135 papers, 54 papers with code

Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRI

1 code implementation13 Sep 2021 Felix Meissen, Georgios Kaissis, Daniel Rueckert

In this work, we tackle the problem of Semi-Supervised Anomaly Segmentation (SAS) in Magnetic Resonance Images (MRI) of the brain, which is the task of automatically identifying pathologies in brain images.

Anomaly Detection

Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

1 code implementation7 Sep 2021 Simon Dahan, Logan Z. J. Williams, Daniel Rueckert, Emma C. Robinson

Results show a prediction accuracy of 94. 4% for sex classification (an increase of 6. 2% compared to other methods), and an improvement of correlation with fluid intelligence of 0. 325 vs 0. 144, relative to a baseline model that encodes space and time separately.

Action Recognition Skeleton Based Action Recognition

PialNN: A Fast Deep Learning Framework for Cortical Pial Surface Reconstruction

1 code implementation6 Sep 2021 Qiang Ma, Emma C. Robinson, Bernhard Kainz, Daniel Rueckert, Amir Alansary

Traditional cortical surface reconstruction is time consuming and limited by the resolution of brain Magnetic Resonance Imaging (MRI).

Voxel-level Importance Maps for Interpretable Brain Age Estimation

no code implementations11 Aug 2021 Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Alexander Hammers, Daniel Rueckert

In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition.

Age Estimation Hippocampus

Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation

1 code implementation7 Aug 2021 Chen Chen, Chen Qin, Cheng Ouyang, 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.

Cardiac Segmentation Data Augmentation

NeuralDP Differentially private neural networks by design

no code implementations30 Jul 2021 Moritz Knolle, Dmitrii Usynin, Alexander Ziller, Marcus R. Makowski, Daniel Rueckert, Georgios Kaissis

The application of differential privacy to the training of deep neural networks holds the promise of allowing large-scale (decentralized) use of sensitive data while providing rigorous privacy guarantees to the individual.

Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation

no code implementations9 Jul 2021 Alexander Ziller, Dmitrii Usynin, Moritz Knolle, Kritika Prakash, Andrew Trask, Rickmer Braren, Marcus Makowski, Daniel Rueckert, Georgios Kaissis

Reconciling large-scale ML with the closed-form reasoning required for the principled analysis of individual privacy loss requires the introduction of new tools for automatic sensitivity analysis and for tracking an individual's data and their features through the flow of computation.

Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation

no code implementations8 Jul 2021 Shuo Wang, Chen Qin, Nicolo Savioli, Chen Chen, Declan O'Regan, Stuart Cook, Yike Guo, Daniel Rueckert, Wenjia Bai

In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.

Cardiac Segmentation Super-Resolution

Differentially private federated deep learning for multi-site medical image segmentation

1 code implementation6 Jul 2021 Alexander Ziller, Dmitrii Usynin, Nicolas Remerscheid, Moritz Knolle, Marcus Makowski, Rickmer Braren, Daniel Rueckert, Georgios Kaissis

The application of PTs to FL in medical imaging and the trade-offs between privacy guarantees and model utility, the ramifications on training performance and the susceptibility of the final models to attacks have not yet been conclusively investigated.

Federated Learning Medical Image Segmentation

Detecting Outliers with Poisson Image Interpolation

no code implementations6 Jul 2021 Jeremy Tan, Benjamin Hou, Thomas Day, John Simpson, Daniel Rueckert, Bernhard Kainz

We propose an alternative to image reconstruction-based and image embedding-based methods and propose a new self-supervised method to tackle pathological anomaly detection.

Anomaly Detection Image Interpolation +1

Cooperative Training and Latent Space Data Augmentation for Robust Medical Image Segmentation

1 code implementation2 Jul 2021 Chen Chen, Kerstin Hammernik, Cheng Ouyang, Chen Qin, Wenjia Bai, Daniel Rueckert

In this paper, we present a cooperative framework for training image segmentation models and a latent space augmentation method for generating hard examples.

Data Augmentation Image Reconstruction +1

Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net

no code implementations19 Jun 2021 Tianrui Liu, Qingjie Meng, Jun-Jie Huang, Athanasios Vlontzos, Daniel Rueckert, Bernhard Kainz

Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames.

Video Summarization

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

Automated Knee X-ray Report Generation

no code implementations22 May 2021 Aydan Gasimova, Giovanni Montana, Daniel Rueckert

Gathering manually annotated images for the purpose of training a predictive model is far more challenging in the medical domain than for natural images as it requires the expertise of qualified radiologists.

Text Generation

HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning

2 code implementations21 May 2021 Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske, Jan Baumbach, Daniel Rueckert, Georgios Kaissis

Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server.

Federated Learning

FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation

no code implementations5 Mar 2021 Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert, Shadi Albarqouni

Further, we illustrate that FedDis learns a shape embedding that is orthogonal to the appearance and consistent under different intensity augmentations.

Anomaly Detection Federated Learning

Personalized Federated Deep Learning for Pain Estimation From Face Images

1 code implementation12 Jan 2021 Ognjen Rudovic, Nicolas Tobis, Sebastian Kaltwang, Björn Schuller, Daniel Rueckert, Jeffrey F. Cohn, Rosalind W. Picard

A potential approach to tackling this is Federated Learning (FL), which enables multiple parties to collaboratively learn a shared prediction model by using parameters of locally trained models while keeping raw training data locally.

Federated Learning

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

Reducing Textural Bias Improves Robustness of Deep Segmentation Models

no code implementations30 Nov 2020 Seoin Chai, Daniel Rueckert, Ahmed E. Fetit

In this thorough empirical study, we draw inspiration from findings on natural images and investigate ways in which addressing the textural bias phenomenon could bring up the robustness of deep segmentation models when applied to three-dimensional (3D) medical data.

Image Classification Semantic Segmentation

2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

no code implementations15 Nov 2020 Harry Cai, Daniel Rueckert, Jonathan Passerat-Palmbach

While the initial model might belong solely to the actor bringing it to the network for training, determining the ownership of the trained model resulting from Federated Learning remains an open question.

Federated Learning

A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

no code implementations10 Nov 2020 Veneta Haralampieva, Daniel Rueckert, Jonathan Passerat-Palmbach

This work provides a comprehensive review of existing frameworks based on secure computing techniques in the context of private image classification.

Classification General Classification +1

Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging

no code implementations30 Oct 2020 Qingjie Meng, Jacqueline Matthew, Veronika A. Zimmer, Alberto Gomez, David F. A. Lloyd, Daniel Rueckert, Bernhard Kainz

To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.

Image Classification

Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcare

no code implementations17 Sep 2020 Matei Grama, Maria Musat, Luis Muñoz-González, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary

In this work, we implement and evaluate different robust aggregation methods in FL applied to healthcare data.

Cryptography and Security

Efficient, high-performance pancreatic segmentation using multi-scale feature extraction

1 code implementation2 Sep 2020 Moritz Knolle, Georgios Kaissis, Friederike Jungmann, Sebastian Ziegelmayer, Daniel Sasse, Marcus Makowski, Daniel Rueckert, Rickmer Braren

For artificial intelligence-based image analysis methods to reach clinical applicability, the development of high-performance algorithms is crucial.

Patch-based Brain Age Estimation from MR Images

no code implementations29 Aug 2020 Kyriaki-Margarita Bintsi, Vasileios Baltatzis, Arinbjörn Kolbeinsson, Alexander Hammers, Daniel Rueckert

Many studies have been proposed for the prediction of chronological age from brain MRI using machine learning and specifically deep learning techniques.

Age Estimation Hippocampus

Causal Future Prediction in a Minkowski Space-Time

no code implementations20 Aug 2020 Athanasios Vlontzos, Henrique Bergallo Rocha, Daniel Rueckert, Bernhard Kainz

In this paper we propose a novel theoretical framework to perform causal future prediction by embedding spatiotemporal information on a Minkowski space-time.

Future prediction Image Generation

Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment

1 code implementation19 Aug 2020 Qingjie Meng, Daniel Rueckert, Bernhard Kainz

The proposed MetFA method explicitly and directly learns the latent representation without using domain adversarial training.

Classification General Classification +2

Geometric Deep Learning for Post-Menstrual Age Prediction based on the Neonatal White Matter Cortical Surface

1 code implementation13 Aug 2020 Vitalis Vosylius, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Antonios Makropoulos, Andreas Schuh, Daniel Rueckert, Amir Alansary

In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter cortical surface.

A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises

no code implementations2 Aug 2020 S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers

In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues.

Realistic Adversarial Data Augmentation for MR Image Segmentation

1 code implementation23 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.

Data Augmentation Medical Image Segmentation

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

no code implementations23 Jun 2020 Shuo Wang, Giacomo Tarroni, Chen Qin, Yuanhan Mo, Chengliang Dai, Chen Chen, Ben Glocker, Yike Guo, Daniel Rueckert, Wenjia Bai

Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.

Medical Image Segmentation

3D Probabilistic Segmentation and Volumetry from 2D projection images

no code implementations23 Jun 2020 Athanasios Vlontzos, Samuel Budd, Benjamin Hou, Daniel Rueckert, Bernhard Kainz

X-Ray imaging is quick, cheap and useful for front-line care assessment and intra-operative real-time imaging (e. g., C-Arm Fluoroscopy).

Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI

1 code implementation8 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.

Image Registration

Ultrasound Video Summarization using Deep Reinforcement Learning

1 code implementation19 May 2020 Tianrui Liu, Qingjie Meng, Athanasios Vlontzos, Jeremy Tan, Daniel Rueckert, Bernhard Kainz

We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.

Video Summarization

Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation

no code implementations7 May 2020 Masahiro Oda, Natsuki Shimizu, Ken'ichi Karasawa, Yukitaka Nimura, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, Kensaku MORI

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information.

Automated Pancreas Segmentation Pancreas Segmentation

$Σ$-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.

Image Enhancement Image Reconstruction +1

Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network

no code implementations7 Oct 2019 Shihao Jin, Nicolò Savioli, Antonio de Marvao, Timothy JW Dawes, Axel Gandy, Daniel Rueckert, Declan P. O'Regan

In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure.

Survival Prediction

Intelligent image synthesis to attack a segmentation CNN using adversarial learning

no code implementations24 Sep 2019 Liang Chen, Paul Bentley, Kensaku MORI, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert

Our approach has three key features: 1) The generated adversarial examples exhibit anatomical variations (in form of deformations) as well as appearance perturbations; 2) The adversarial examples attack segmentation models so that the Dice scores decrease by a pre-specified amount; 3) The attack is not required to be specified beforehand.

Image Generation Semantic Segmentation

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.

Model-Based and Data-Driven Strategies in Medical Image Computing

no code implementations23 Sep 2019 Daniel Rueckert, Julia A. Schnabel

With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis and interpretation.

Image Reconstruction Transfer Learning

Flexible Conditional Image Generation of Missing Data with Learned Mental Maps

no code implementations29 Aug 2019 Benjamin Hou, Athanasios Vlontzos, Amir Alansary, Daniel Rueckert, Bernhard Kainz

Real-world settings often do not allow acquisition of high-resolution volumetric images for accurate morphological assessment and diagnostic.

Conditional Image Generation Decision Making +1

Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound

1 code implementation21 Aug 2019 Qingjie Meng, Nick Pawlowski, Daniel Rueckert, Bernhard Kainz

These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learning algorithms.

Multi-Task Learning

Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation

no code implementations20 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.

Semantic Segmentation Style Transfer

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

no code implementations20 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.

Image Reconstruction Motion Estimation

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

no code implementations13 Aug 2019 Esther Puyol-Antón, Bram Ruijsink, James R. Clough, Ilkay Oksuz, Daniel Rueckert, Reza Razavi, Andrew P. King

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health.

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

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

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

Improving the generalizability of convolutional neural network-based segmentation on CMR images

1 code implementation2 Jul 2019 Chen Chen, Wenjia Bai, Rhodri H. Davies, Anish N. Bhuva, Charlotte Manisty, James C. Moon, Nay Aung, Aaron M. Lee, Mihir M. Sanghvi, Kenneth Fung, Jose Miguel Paiva, Steffen E. Petersen, Elena Lukaschuk, Stefan K. Piechnik, Stefan Neubauer, Daniel Rueckert

We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.

Semantic Segmentation

Multiple Landmark Detection using Multi-Agent Reinforcement Learning

1 code implementation30 Jun 2019 Athanasios Vlontzos, Amir Alansary, Konstantinos Kamnitsas, Daniel Rueckert, Bernhard Kainz

We compare our approach with state-of-the-art architectures and achieve significantly better accuracy by reducing the detection error by 50%, while requiring fewer computational resources and time to train compared to the naive approach of training K agents separately.

Multi-agent Reinforcement Learning

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.

Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes

no code implementations19 Apr 2019 Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard

We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months.


Unsupervised Deformable Registration for Multi-Modal Images via Disentangled Representations

no code implementations22 Mar 2019 Chen Qin, Bibo Shi, Rui Liao, Tommaso Mansi, Daniel Rueckert, Ali Kamen

The proposed registration approach is then built on the factorized latent shape code, with the assumption that the intrinsic shape deformation existing in original image domain is preserved in this latent space.

Image Registration Image-to-Image Translation

Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review

no code implementations20 Dec 2018 Juan J. Cerrolaza, Mirella Lopez-Picazo, Ludovic Humbert, Yoshinobu Sato, Daniel Rueckert, Miguel Angel Gonzalez Ballester, Marius George Linguraru

With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.

GANsfer Learning: Combining labelled and unlabelled data for GAN based data augmentation

no code implementations26 Nov 2018 Christopher Bowles, Roger Gunn, Alexander Hammers, Daniel Rueckert

We also show how a shift in domain of the training data from young and healthy towards older and more pathological examples leads to better segmentations of the latter cases, and that this leads to a significant improvement in the ability for the computed segmentations to stratify cases of AD.

Data Augmentation

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Multi-Task Learning for Left Atrial Segmentation on GE-MRI

1 code implementation31 Oct 2018 Chen Chen, Wenjia Bai, Daniel Rueckert

Segmentation of the left atrium (LA) is crucial for assessing its anatomy in both pre-operative atrial fibrillation (AF) ablation planning and post-operative follow-up studies.

General Classification Multi-Task Learning

Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning

no code implementations29 Oct 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, James Clough, Gastao Cruz, Aurelien Bustin, Claudia Prieto, Rene Botnar, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.

Curriculum Learning Data Augmentation +3

A Comprehensive Approach for Learning-based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks

no code implementations3 Oct 2018 Giacomo Tarroni, Ozan Oktay, Matthew Sinclair, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Antonio de Marvao, Declan O'Regan, Stuart Cook, Daniel Rueckert

If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks.

Motion Compensation

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

Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images

2 code implementations22 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.

General Classification Image Classification

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

no code implementations15 Aug 2018 Ilkay Oksuz, Bram Ruijsink, Esther Puyol-Anton, Aurelien Bustin, Gastao Cruz, Claudia Prieto, Daniel Rueckert, Julia A. Schnabel, Andrew P. King

As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.

Data Augmentation Image Quality Assessment +1

Recurrent neural networks for aortic image sequence segmentation with sparse annotations

no code implementations1 Aug 2018 Wenjia Bai, Hideaki Suzuki, Chen Qin, Giacomo Tarroni, Ozan Oktay, Paul M. Matthews, Daniel Rueckert

In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task.

Small Organ Segmentation in Whole-body MRI using a Two-stage FCN and Weighting Schemes

no code implementations30 Jul 2018 Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.

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).

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

1 code implementation28 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.

MRI Reconstruction

Fast Multiple Landmark Localisation Using a Patch-based Iterative Network

1 code implementation18 Jun 2018 Yuanwei Li, Amir Alansary, Juan J. Cerrolaza, Bishesh Khanal, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, Daniel Rueckert

PIN is computationally efficient since the inference stage only selectively samples a small number of patches in an iterative fashion rather than a dense sampling at every location in the volume.

Multi-Task Learning

Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

1 code implementation11 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.

Cardiac Segmentation Motion Estimation +1

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

1 code implementation11 Jun 2018 Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker

NeuroNet is a deep convolutional neural network mimicking multiple popular and state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.

Brain Image Segmentation Brain Segmentation +1

Semi-Supervised Learning via Compact Latent Space Clustering

no code implementations ICML 2018 Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc, Ian Walker, Ryutaro Tanno, Daniel Rueckert, Ben Glocker, Antonio Criminisi, Aditya Nori

We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation.

Graph Saliency Maps through Spectral Convolutional Networks: Application to Sex Classification with Brain Connectivity

no code implementations5 Jun 2018 Salim Arslan, Sofia Ira Ktena, Ben Glocker, Daniel Rueckert

Graph convolutional networks (GCNs) allow to apply traditional convolution operations in non-Euclidean domains, where data are commonly modelled as irregular graphs.

General Classification

Attention-Gated Networks for Improving Ultrasound Scan Plane Detection

6 code implementations15 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.

Attention U-Net: Learning Where to Look for the Pancreas

23 code implementations11 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.

Pancreas Segmentation

Learning-Based Quality Control for Cardiac MR Images

no code implementations25 Mar 2018 Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P. O'Regan, Stuart Cook, Ben Glocker, Paul M. Matthews, Daniel Rueckert

The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e. g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.

Motion Detection

Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction

3 code implementations5 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.

Image Reconstruction

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

1 code implementation18 Nov 2017 Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl

We present DLTK, a toolkit providing baseline implementations for efficient experimentation with deep learning methods on biomedical images.

Semantic Segmentation

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

no code implementations4 Nov 2017 Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation.

Semantic Segmentation

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks

1 code implementation25 Oct 2017 Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert

By combining FCN with a large-scale annotated dataset, the proposed automated method achieves a high performance on par with human experts in segmenting the LV and RV on short-axis CMR images and the left atrium (LA) and right atrium (RA) on long-axis CMR images.

3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images

no code implementations19 Sep 2017 Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

We extensively evaluate the effectiveness of our approach quantitatively on simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic motion and further demonstrate qualitative results on real fetal MRI data where our method is integrated into a full reconstruction and motion compensation pipeline.

3D Reconstruction Image Reconstruction +2

Is human face processing a feature- or pattern-based task? Evidence using a unified computational method driven by eye movements

no code implementations4 Sep 2017 Carlos E. Thomaz, Vagner Amaral, Gilson A. Giraldi, Duncan F. Gillies, Daniel Rueckert

Research on human face processing using eye movements has provided evidence that we recognize face images successfully focusing our visual attention on a few inner facial regions, mainly on the eyes, nose and mouth.

Eye Tracking Facial Expression Recognition

Employing Weak Annotations for Medical Image Analysis Problems

no code implementations21 Aug 2017 Martin Rajchl, Lisa M. Koch, Christian Ledig, Jonathan Passerat-Palmbach, Kazunari Misawa, Kensaku MORI, Daniel Rueckert

To efficiently establish training databases for machine learning methods, collaborative and crowdsourcing platforms have been investigated to collectively tackle the annotation effort.

Computed Tomography (CT) Liver Segmentation

A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

3 code implementations8 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.

Dictionary Learning Image Reconstruction

Exploring Heritability of Functional Brain Networks with Inexact Graph Matching

no code implementations29 Mar 2017 Sofia Ira Ktena, Salim Arslan, Sarah Parisot, Daniel Rueckert

Data-driven brain parcellations aim to provide a more accurate representation of an individual's functional connectivity, since they are able to capture individual variability that arises due to development or disease.

Graph Matching

Spectral Graph Convolutions for Population-based Disease Prediction

1 code implementation8 Mar 2017 Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrerro Moreno, Ben Glocker, Daniel Rueckert

We demonstrate the potential of the method on the challenging ADNI and ABIDE databases, as a proof of concept of the benefit from integrating contextual information in classification tasks.

Disease Prediction

Distance Metric Learning using Graph Convolutional Networks: Application to Functional Brain Networks

3 code implementations7 Mar 2017 Sofia Ira Ktena, Sarah Parisot, Enzo Ferrante, Martin Rajchl, Matthew Lee, Ben Glocker, Daniel Rueckert

Evaluating similarity between graphs is of major importance in several computer vision and pattern recognition problems, where graph representations are often used to model objects or interactions between elements.

Graph Similarity Metric Learning

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

1 code implementation28 Feb 2017 Benjamin Hou, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz

Our approach is attractive in challenging imaging scenarios, where significant subject motion complicates reconstruction performance of 3D volumes from 2D slice data.

Image Registration Motion Compensation

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

no code implementations11 Feb 2017 Vanya V. Valindria, Ioannis Lavdas, Wenjia Bai, Konstantinos Kamnitsas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

In RCA we take the predicted segmentation from a new image to train a reverse classifier which is evaluated on a set of reference images with available ground truth.

General Classification

Unsupervised domain adaptation in brain lesion segmentation with adversarial networks

no code implementations28 Dec 2016 Konstantinos Kamnitsas, Christian Baumgartner, Christian Ledig, Virginia F. J. Newcombe, Joanna P. Simpson, Andrew D. Kane, David K. Menon, Aditya Nori, Antonio Criminisi, Daniel Rueckert, Ben Glocker

In this work we investigate unsupervised domain adaptation using adversarial neural networks to train a segmentation method which is more invariant to differences in the input data, and which does not require any annotations on the test domain.

Lesion Segmentation Unsupervised Domain Adaptation

SonoNet: Real-Time Detection and Localisation of Fetal Standard Scan Planes in Freehand Ultrasound

2 code implementations16 Dec 2016 Christian F. Baumgartner, Konstantinos Kamnitsas, Jacqueline Matthew, Tara P. Fletcher, Sandra Smith, Lisa M. Koch, Bernhard Kainz, Daniel Rueckert

In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box.

Comparison of Brain Networks with Unknown Correspondences

no code implementations15 Nov 2016 Sofia Ira Ktena, Sarah Parisot, Jonathan Passerat-Palmbach, Daniel Rueckert

In this work we explore a method based on graph edit distance for evaluating graph similarity, when correspondences between network elements are unknown due to different underlying subdivisions of the brain.

Graph Similarity

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

no code implementations29 Apr 2016 Lisa M. Koch, Martin Rajchl, Wenjia Bai, Christian F. Baumgartner, Tong Tong, Jonathan Passerat-Palmbach, Paul Aljabar, Daniel Rueckert

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets.

Cardiac Segmentation

Patch-based Evaluation of Image Segmentation

no code implementations CVPR 2014 Christian Ledig, Wenzhe Shi, Wenjia Bai, Daniel Rueckert

The ideal similarity measure should be unbiased to segmentations of different volume and complexity, and be able to quantify and visualise segmentation bias.

Hippocampus Semantic Segmentation

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