Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
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698 results found

Alansary A, Rajchl M, McDonagh S, Murgasova M, Damodaram M, Lloyd DFA, Davidson A, Rutherford M, Hajnal JV, Rueckert D, Kainz Bet al., 2017, PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 2031-2044, ISSN: 1558-254X

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a \emph{single} investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in the presence of affine motion artifacts compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. We further evaluate the distance error for selected anatomical landmarks in the fetal head, as well as calculating the mean and maximum displacements resulting from automatic non-rigid registration to a motion-free ground truth image. These experiments demonstrate a successful application of PVR motion compensation to the whole fetal body, uterus and placenta.

Journal article

Zimmer VA, Glocker B, Hahner N, Eixarch E, Sanroma G, Gratacos E, Rueckert D, Angel Gonzalez Ballester M, Piella Get al., 2017, Learning and combining image neighborhoods using random forests forneonatal brain disease classification, Medical Image Analysis, Vol: 42, Pages: 189-199, ISSN: 1361-8423

It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defined distance. We propose a framework to learn multiple pairwise distances in a population of brain images and to combine them in an unsupervised manner optimally in a manifold learning step. Unlike other methods that only use a univariate distance measure, our method allows for a natural combination of multiple distances from heterogeneous sources. As a result, it yields a representation of the population that preserves the multiple distances. Furthermore, our method also selects the most predictive features associated with the distances. We evaluate our method in neonatal magnetic resonance images of three groups (term controls, patients affected by intrauterine growth restriction and mild isolated ventriculomegaly). We show that combining multiple distances related to the condition improves the overall characterization and classification of the three clinical groups compared to the use of single distances and classical unsupervised manifold learning.

Journal article

Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AG, Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, Taylor SA, Aboagye EO, Rockall AGet al., 2017, Fully automatic, multi-organ segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs) and a multi-atlas (MA) approach., Medical Physics, Vol: 44, Pages: 5210-5220, ISSN: 0094-2405

PURPOSE: As part of a programme to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated and compared three algorithms for fully automatic, multi-organ segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardised, multi-parametric whole body MRI protocol at 1.5T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Five-fold cross-validation experiments were run on 34 artefact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the Dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root mean square surface distance (RMSSD) and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of data sets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC=0.70±0.18, RE=0.73±0.18, PR=0.71±0.14, CNNs: DSC=0.81±0.13, RE=0.83±0.14, PR=0.82±0.10, MA: DSC=0.71±0.22, RE=0.70±0.34

Journal article

Baumgartner C, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch L, Kainz B, Rueckert Det al., 2017, SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 2204-2215, ISSN: 1558-254X

Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. 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. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.

Journal article

Dawes T, de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det al., 2017, Systolic motion of the basal right ventricular freewall is the strongest predictor of global function: a high resolution 3D imaging study, Association-of-Anaesthetists-of-Great-Britain-and-Ireland (AAGBI) GAT Annual Scientific Meeting, Publisher: Wiley, Pages: 77-77, ISSN: 0003-2409

Conference paper

Makropoulos A, Counsell SJ, Rueckert D, 2017, A review on automatic fetal and neonatal brain MRI segmentation, NeuroImage, Vol: 170, Pages: 231-248, ISSN: 1053-8119

In recent years, a variety of segmentation methods have been proposed for automatic delineation of the fetal and neonatal brain MRI. These methods aim to define regions of interest of different granularity: brain, tissue types or more localised structures. Different methodologies have been applied for this segmentation task and can be classified into unsupervised, parametric, classification, atlas fusion and deformable models. Brain atlases are commonly utilised as training data in the segmentation process. Challenges relating to the image acquisition, the rapid brain development as well as the limited availability of imaging data however hinder this segmentation task. In this paper, we review methods adopted for the perinatal brain and categorise them according to the target population, structures segmented and methodology. We outline different methods proposed in the literature and discuss their major contributions. Different approaches for the evaluation of the segmentation accuracy and benchmarks used for the segmentation quality are presented. We conclude this review with a discussion on shortcomings in the perinatal domain and possible future directions.

Journal article

Ktena SI, Arslan S, Parisot S, Rueckert Det al., 2017, Exploring heritability of functional brain networks with inexact graph matching, IEEE 14th International Symposium on Biomedical Imaging, Publisher: IEEE, Pages: 354-357, ISSN: 1945-7928

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. This renders comparisons between the emerging brain connectivity networks more challenging, since correspondences between their elements are not preserved. Unveiling these correspondences is of major importance to keep track of local functional connectivity changes. We propose a novel method based on graph edit distance for the comparison of brain graphs directly in their domain, that can accurately reflect similarities between individual networks while providing the network element correspondences. This method is validated on a dataset of 116 twin subjects provided by the Human Connectome Project.

Conference paper

Chen L, Bentley P, Rueckert D, 2017, Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks, NeuroImage: Clinical, Vol: 15, Pages: 633-643, ISSN: 2213-1582

Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifyingthem manually is costly and challenging for clinicians. In this paper, we propose a novel framework to auto-matically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs):one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

Journal article

Tong T, Ledig C, Guerrero R, Schuh A, Koikkalainen J, Tolonen A, Rhodius H, Barkhof F, Tijms B, Lemstra AW, Soininen H, Remes AM, Waldemar G, Hasselbalch S, Mecocci P, Baroni M, Lotjonen J, van der Flierd W, Rueckert Det al., 2017, Five-class differential diagnostics of neurodegenerative diseases using random undersampling boosting, NeuroImage: Clinical, Vol: 15, Pages: 613-624, ISSN: 2213-1582

Differentiating between different types of neurodegenerative diseases is not only crucial in clinical practice when treatment decisions have to be made, but also has a significant potential for the enrichment of clinical trials. The purpose of this study is to develop a classification framework for distinguishing the four most common neurodegenerative diseases, including Alzheimer's disease, frontotemporal lobe degeneration, Dementia with Lewy bodies and vascular dementia, as well as patients with subjective memory complaints. Different biomarkers including features from images (volume features, region-wise grading features) and non-imaging features (CSF measures) were extracted for each subject. In clinical practice, the prevalence of different dementia types is imbalanced, posing challenges for learning an effective classification model. Therefore, we propose the use of the RUSBoost algorithm in order to train classifiers and to handle the class imbalance training problem. Furthermore, a multi-class feature selection method based on sparsity is integrated into the proposed framework to improve the classification performance. It also provides a way for investigating the importance of different features and regions. Using a dataset of 500 subjects, the proposed framework achieved a high accuracy of 75.2% with a balanced accuracy of 69.3% for the five-class classification using ten-fold cross validation, which is significantly better than the results using support vector machine or random forest, demonstrating the feasibility of the proposed framework to support clinical decision making.

Journal article

Kamnitsas K, Baumgartner C, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, Nori A, Criminisi A, Rueckert D, Glocker Bet al., 2017, Unsupervised domain adaptation in brain lesion segmentation with adversarial networks, Information Processing in Medical Imaging, Publisher: Springer

Significant advances have been made towards building accu-rate automatic segmentation systems for a variety of biomedical applica-tions using machine learning. However, the performance of these systemsoften degrades when they are applied on new data that differ from thetraining data, for example, due to variations in imaging protocols. Man-ually annotating new data for each test domain is not a feasible solution.In this work we investigate unsupervised domain adaptation using ad-versarial neural networks to train a segmentation method which is moreinvariant to differences in the input data, and which does not require anyannotations on the test domain. Specifically, we learn domain-invariantfeatures by learning to counter an adversarial network, which attemptsto classify the domain of the input data by observing the activations ofthe segmentation network. Furthermore, we propose a multi-connecteddomain discriminator for improved adversarial training. Our system isevaluated using two MR databases of subjects with traumatic brain in-juries, acquired using different scanners and imaging protocols. Usingour unsupervised approach, we obtain segmentation accuracies whichare close to the upper bound of supervised domain adaptation.

Conference paper

Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, Glocker B, de Marvao A, O'Regan D, Cook S, Rueckert Det al., 2017, Learning-based heart coverage estimation for short-axis cine cardiac MR images, Functional Imaging and Modelling of the Heart (FIMH), Publisher: Springer

The correct acquisition of short axis (SA) cine cardiac MRimage stacks requires the imaging of the full cardiac anatomy betweenthe apex and the mitral valve plane via multiple 2D slices. While in theclinical practice the SA stacks are usually checked qualitatively to en-sure full heart coverage, visual inspection can become infeasible for largeamounts of imaging data that is routinely acquired, e.g. in populationstudies such as the UK Biobank (UKBB). Accordingly, we propose alearning-based technique for the fully-automated estimation of the heartcoverage for SA image stacks. The technique relies on the identificationof cardiac landmarks (i.e. the apex and the mitral valve sides) on twochamber view long axis images and on the comparison of the landmarks’positions to the volume covered by the SA stack. Landmark detection isperformed using a hybrid random forest approach integrating both re-gression and structured classification models. The technique was appliedon 3000 cases from the UKBB and compared to visual assessment. Theobtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicatethat the proposed technique is able to correctly detect the vast majorityof the cases with insufficient coverage, suggesting that it could be usedas a fully-automated quality control step for CMR SA image stacks.

Conference paper

Dawes T, Simoes monteiro de marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs J, Rueckert D, Cook S, Wilkins M, O'Regan DPet al., 2017, Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, Vol: 283, Pages: 381-390, ISSN: 1527-1315

Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.

Journal article

Toisoul A, Rueckert D, Kainz B, 2017, Accessible GLSL Shader programming, EuroGraphics 2017, ISSN: 1017-4656

Teaching fundamental principles of Computer Graphics requires a thoroughly prepared lecture alongside practical training.Modern graphics programming rarely provides a straightforward application programming interface (API) and the availableAPIs pose high entry barriers to students. Shader-based programming of standard graphics pipelines is often inaccessiblethrough complex setup procedures and convoluted programming environments. In this paper we discuss an undergraduateentry level lecture with its according lab exercises. We present a programming framework that makes interactive graphicsprogramming accessible while allowing to design individual tasks as instructive exercises to solidify the content of individuallecture units. The discussed teaching framework provides a well defined programmable graphics pipeline with geometry shadingstages and image-based post processing functionality based on framebuffer objects. It is open-source and available online.

Conference paper

Valindria V, Lavdas I, Bai W, Kamnitsas K, Aboagye E, Rockall A, Rueckert D, Glocker Bet al., 2017, Reverse classification accuracy: predicting segmentation performance in the absence of ground truth, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 1597-1606, ISSN: 1558-254X

When integrating computational tools such as au-tomatic segmentation into clinical practice, it is of utmostimportance to be able to assess the level of accuracy on newdata, and in particular, to detect when an automatic methodfails. However, this is difficult to achieve due to absence of groundtruth. Segmentation accuracy on clinical data might be differentfrom what is found through cross-validation because validationdata is often used during incremental method development, whichcan lead to overfitting and unrealistic performance expectations.Before deployment, performance is quantified using differentmetrics, for which the predicted segmentation is compared toa reference segmentation, often obtained manually by an expert.But little is known about the real performance after deploymentwhen a reference is unavailable. In this paper, we introduce theconcept ofreverse classification accuracy(RCA) as a frameworkfor predicting the performance of a segmentation method onnew data. In RCA we take the predicted segmentation froma new image to train a reverse classifier which is evaluatedon a set of reference images with available ground truth. Thehypothesis is that if the predicted segmentation is of good quality,then the reverse classifier will perform well on at least some ofthe reference images. We validate our approach on multi-organsegmentation with different classifiers and segmentation methods.Our results indicate that it is indeed possible to predict the qualityof individual segmentations, in the absence of ground truth. Thus,RCA is ideal for integration into automatic processing pipelines inclinical routine and as part of large-scale image analysis studies.

Journal article

Arslan S, Ktena SI, Makropoulos A, Robinson EC, Rueckert D, Parisot Set al., 2017, Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex, Neuroimage, Vol: 170, Pages: 5-30, ISSN: 1095-9572

The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.

Journal article

Kamnitsas K, Ferrante E, Parisot S, Ledig C, Nori AV, Criminisi A, Rueckert D, Glocker Bet al., 2017, DeepMedic for brain tumor segmentation, Second International Workshop, BrainLes 2016, with the Challenges on BRATS, ISLES and mTOP 2016 Held in Conjunction with MICCAI 2016, Publisher: Springer, Pages: 138-149, ISSN: 0302-9743

Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.

Conference paper

Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, Zheng G, Rueckert D, Mori Ket al., 2017, Multi-atlas pancreas segmentation: Atlas selection based on vessel structure, Medical Image Analysis, Vol: 39, Pages: 18-28, ISSN: 1361-8415

Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%.

Journal article

Ciller C, De Zanet S, Kamnitsas K, Maeder P, Glocker B, Munier FL, Rueckert D, Thiran J-P, Cuadra MB, Sznitman Ret al., 2017, Multi-channel MRI segmentation of eye structures and tumors using patient-specific features, PLOS ONE, Vol: 12, ISSN: 1932-6203

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.

Journal article

Passerat-Palmbach J, Reuillon RR, Leclaire ML, Makropoulos AM, Robinson ER, Parisot SP, Rueckert DRet al., 2017, Reproducible large-scale neuroimaging studies with the OpenMOLE workflow management system, Frontiers in Neuroinformatics, Vol: 11, ISSN: 1662-5196

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution.Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments.OpenMOLE hides the complexity of designing complex experiments thanks to its DSL. Users can embed their own applications and scale their pipelines from a small prototype running on their desktop computer to a large-scale study harnessing distributed computing infrastructures, simply by changing a single line in the pipeline definition.The construction of the pipeline itself is decoupled from the execution context. The high-level DSL abstracts the underlying execution environment, contrary to classic shell-script based pipelines. These two aspects allow pipelines to be shared and studies to be replicated across different computing environments.Workflows can be run as traditional batch pipelines or coupled with OpenMOLE's advanced exploration methods in order to study the behaviour of an application, or perform automatic parameter tuning.In this work, we briefly present the strong assets of OpenMOLE and detail recent improvements targeting re-executability of workflows across various Linux platforms. We have tightly coupled OpenMOLE with CARE, a standalone containerisation solution that allows re-executing on a Linux host any application that has been packaged on another Linux host previously.The solution is evaluated against a Python-based pipeline involving packages such as scikit-learn as well as binary dependencies. All were packaged and re-executed successfully on various HPC environments, with identical numerical results (here prediction scores) obtained on each environment. Our results show that the pair formed by OpenMOLE and CARE is a reliable solution to generate reproducible results and re-executable pipelines. A demonstrati

Journal article

Giannakidis A, Oktay O, Keegan J, Spadotto V, Voges I, Smith G, Pierce I, Bai W, Rueckert D, Ernst S, Gatzoulis MA, Pennell DJ, Babu-Narayan S, Firmin DNet al., Super-resolution Reconstruction of Late Gadolinium Cardiovascular Magnetic Resonance Images using a Residual Convolutional Neural Network, The 25th Scientific Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2017)

Conference paper

Schuh A, Makropoulos A, Wright R, Robinson EC, Tusor N, Steinweg J, Hughes E, Cordero Grande L, Price A, Hutter J, Hajnal JV, Rueckert Det al., A deformable model for the reconstruction of the neonatal cortex, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI)

We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based refinement. We show that the obtained surface models define the cortical boundaries more accurately than the segmentation. The surface meshes are further guaranteed to not intersect and subdivide the brain volume into disjoint regions. The proposed method generates topologically correct surfaces which facilitate both a flattening and spherical mapping of the cortex.

Conference paper

Sinclair M, Peressutti D, Puyol-Anton E, Bai W, Nordsletten D, Hadjicharalambous M, Kerfoot E, Jackson T, Claridge S, Rinaldi CA, Rueckert D, King APet al., 2017, Learning Optimal Spatial Scales for Cardiac Strain Analysis Using a Motion Atlas, 7th International Workshop on Statistical Atlases and Computational Models of the Heart (STACOM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 57-65, ISSN: 0302-9743

Conference paper

Schlemper J, Caballero J, Hajnal JV, Price A, Rueckert Det al., 2017, A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction, 25th Biennial International Conference on Information Processing in Medical Imaging (IPMI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 647-658, ISSN: 0302-9743

Conference paper

Baumgartner CF, Oktay O, Rueckert D, 2017, Fully convolutional networks in medical imaging: Applications to image enhancement and recognition, Advances in Computer Vision and Pattern Recognition, Pages: 159-179

© Springer International Publishing Switzerland 2017. Convolutional neural networks (CNNs) are hierarchical models that have immense representational capacity and have been successfully applied to computer vision problems including object localisation, classification and super-resolution. A particular example of CN Nmodels, knownas fully convolutional network (FCN), has been shown to offer improved computational efficiency and representation learning capabilities due to simplermodel parametrisation and spatial consistency of extracted features. In this chapter, we demonstrate the power and applicability of this particular model on two medical imaging tasks, image enhancement via super-resolution and image recognition. In both examples, experimental results show that FCN models can significantly outperform traditional learning-based approaches while achieving real-time performance. Additionally, we demonstrate that the proposed image classification FCN model can be used in organ localisation task as well without requiring additional training data.

Book chapter

Oda M, Shimizu N, Roth HR, Karasawa K, Kitasaka T, Misawa K, Fujiwara M, Rueckert D, Mori Ket al., 2017, 3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation, 3rd MICCAI International Workshop on Deep Learning in Medical Image Analysis (DLMIA) / 7th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 222-230, ISSN: 0302-9743

Conference paper

Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee MCH, Glocker B, Rueckert Det al., 2017, Distance Metric Learning Using Graph Convolutional Networks: Application to Functional Brain Networks., Publisher: Springer, Pages: 469-477

Conference paper

Lorch B, Vaillant G, Baumgartner C, Bai W, Rueckert D, Maier Aet al., 2017, Automated Detection of Motion Artefacts in MR Imaging Using Decision Forests., J Med Eng, Vol: 2017, ISSN: 2314-5137

The acquisition of a Magnetic Resonance (MR) scan usually takes longer than subjects can remain still. Movement of the subject such as bulk patient motion or respiratory motion degrades the image quality and its diagnostic value by producing image artefacts like ghosting, blurring, and smearing. This work focuses on the effect of motion on the reconstructed slices and the detection of motion artefacts in the reconstruction by using a supervised learning approach based on random decision forests. Both the effects of bulk patient motion occurring at various time points in the acquisition on head scans and the effects of respiratory motion on cardiac scans are studied. Evaluation is performed on synthetic images where motion artefacts have been introduced by altering the k-space data according to a motion trajectory, using the three common k-space sampling patterns: Cartesian, radial, and spiral. The results suggest that a machine learning approach is well capable of learning the characteristics of motion artefacts and subsequently detecting motion artefacts with a confidence that depends on the sampling pattern.

Journal article

Robinson E, Glocker B, Rajchl M, Rueckert Det al., 2016, Discrete Optimisation for Group-wise Cortical Surface Atlasing, International Workshop on Biomedical Image Registration, Publisher: IEEE, ISSN: 2160-7516

This paper presents a novel method for cortical surfaceatlasing. Group-wise registration is performed through adiscrete optimisation framework that seeks to simultaneouslyimprove pairwise correspondences between surfacefeature sets, whilst minimising a global cost relating to therank of the feature matrix. It is assumed that when fullyaligned, features will be highly linearly correlated, andthus have low rank. The framework is regularised throughuse of multi-resolution control point grids and higher-ordersmoothness terms, calculated by considering deformationstrain for displacements of triplets of points. Accordinglythe discrete framework is solved through high-order cliquereduction. The framework is tested on cortical foldingbased alignment, using data from the Human ConnectomeProject. Preliminary results indicate that group-wise alignmentimproves folding correspondences, relative to registrationbetween all pair-wise combinations, and registrationto a global average template.

Conference paper

Gomez A, Oktay O, Rueckert D, Penney GP, Schnabel JA, Simpson JM, Pushparajah Ket al., 2016, Regional differences in end-diastolic volumes between 3D echo and CMR in HLHS patients, Frontiers in Pediatrics, Vol: 4, ISSN: 2296-2360

Ultrasound is commonly thought to underestimate ventricular volumes compared to magnetic resonance imaging (MRI), although the reason for this and the spatial distribution of the volume difference is not well understood. In this paper, we use landmark-based image registration to spatially align MRI and ultrasound images from patients with hypoplastic left heart syndrome and carry out a qualitative and quantitative spatial comparison of manual segmentations of the ventricular volume obtained from the respective modalities. In our experiments, we have found a trend showing volumes estimated from ultrasound to be smaller than those obtained from MRI (by approximately up to 20 ml), and that important contributors to this difference are the presence of artifacts such as shadows in the echo images and the different criteria to include or exclude image features as part of the ventricular volume.

Journal article

Xie Z, 2016, Machine learning for efficient recognition of anatomical structures and abnormalities in biomedical images

Thesis dissertation

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