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
to

715 results found

Heckemann RA, Keihaninejad S, Aljabar P, Gray KR, Nielsen C, Rueckert D, Hajnal JV, Hammers Aet al., 2011, A REPOSITORY OF MR MORPHOMETRY DATA IN ALZHEIMER'S DISEASE AND MILD COGNITIVE IMPAIRMENT, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 875-878, ISSN: 1945-7928

Conference paper

Guerrero R, Wolz R, Rueckert D, 2011, Laplacian Eigenmaps Manifold Learning for Landmark Localization in Brain MR Images, 14th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), Publisher: SPRINGER-VERLAG BERLIN, Pages: 566-573, ISSN: 0302-9743

Conference paper

Zhang DP, Zhuang X, Ourselin S, Rueckert Det al., 2011, Motion tracking of left ventricle and coronaries in 4D CTA, Conference on Medical Imaging 2011 - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X

Conference paper

Luong DVN, Rueckert D, Rustem B, 2011, Incorporating hard constraints into non-rigid registration via nonlinear programming, Conference on Medical Imaging 2011 - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X

Conference paper

Donoghue C, Rao A, Bull AMJ, Rueckert Det al., 2011, Manifold learning for automatically predicting articular cartilage morphology in the knee with data from the osteoarthritis initiative (OAI), Conference on Medical Imaging 2011 - Image Processing, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X

Conference paper

Deligianni F, Varoquaux G, Thirion B, Robinson E, Sharp DJ, Edwards AD, Rueckert Det al., 2011, A Probabilistic Framework to Infer Brain Functional Connectivity from Anatomical Connections, 22nd International Conference on Information Processing in Medical Imaging (IPMI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 296-307, ISSN: 0302-9743

Conference paper

Garg R, Pizarro L, Rueckert D, Agapito Let al., 2011, Dense Multi-frame Optic Flow for Non-rigid Objects Using Subspace Constraints, 10th Asian Conference on Computer Vision (ACCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 460-+, ISSN: 0302-9743

Conference paper

Chen B, Naito H, Nakamura Y, Kitasaka T, Rueckert D, Honma H, Takabatake H, Mori M, Natori H, Mori Ket al., 2011, Automatic Segmentation and Identification of Solitary Pulmonary Nodules on Follow-up CT Scans Based on Local Intensity Structure Analysis and Non-rigid Image Registration, Conference on Medical Imaging 2011 - Computer-Aided Diagnosis, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X

Conference paper

Karim R, Arujuna A, Brazier A, Gill J, Rinaldi CA, O'Neill M, Razavi R, Schaeffter T, Rueckert D, Rhode KSet al., 2011, Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance Imaging, 6th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 63-70, ISSN: 0302-9743

Conference paper

Xi J, Lamata P, Shi W, Niederer S, Land S, Rueckert D, Duckett SG, Shetty AK, Rinaldi CA, Razavi R, Smith Net al., 2011, An Automatic Data Assimilation Framework for Patient-Specific Myocardial Mechanical Parameter Estimation, 6th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 392-400, ISSN: 0302-9743

Conference paper

Zhuang X, Shi W, Duckett S, Wang H, Razavi R, Hawkes D, Rueckert D, Ourselin Set al., 2011, A Framework Combining Multi-sequence MRI for Fully Automated Quantitative Analysis of Cardiac Global And Regional Functions, 6th International Conference on Functional Imaging and Modeling of the Heart (FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 367-374, ISSN: 0302-9743

Conference paper

Darvann TA, Hermann NV, Demant S, Larsen P, Olafsdottir H, Thorup SS, Zak M, Lipira AB, Kane AA, Govier D, Schatz H, Rueckert D, Kreiborg Set al., 2011, AUTOMATED QUANTIFICATION AND ANALYSIS OF FACIAL ASYMMETRY IN CHILDREN WITH ARTHRITIS IN THE TEMPOROMANDIBULAR JOINT, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1193-1196, ISSN: 1945-7928

Conference paper

Lotjonen J, Wolz R, Koikkalainen J, Thurfjell L, Lundqvist R, Waldemar G, Soininen H, Rueckert Det al., 2011, IMPROVED GENERATION OF PROBABILISTIC ATLASES FOR THE EXPECTATION MAXIMIZATION CLASSIFICATION, 8th IEEE International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro, Publisher: IEEE, Pages: 1839-1842, ISSN: 1945-7928

Conference paper

Aljabar P, Wolz R, Srinivasan L, Counsell S, Boardman JP, Murgasova M, Doria V, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det al., 2010, Combining morphological information in a manifold learning framework: application to neonatal MRI., Pages: 1-8

MR image data can provide many features or measures although any single measure is unlikely to comprehensively characterize the underlying morphology. We present a framework in which multiple measures are used in manifold learning steps to generate coordinate embeddings which are then combined to give an improved single representation of the population. An application to neonatal brain MRI data shows that the use of shape and appearance measures in particular leads to biologically plausible and consistent representations correlating well with clinical data. Orthogonality among the correlations suggests the embedding components relate to comparatively independent morphological features. The rapid changes that occur in brain shape and in MR image appearance during neonatal brain development justify the use of shape measures (obtained from a deformation metric) and appearance measures (obtained from image similarity). The benefit of combining separate embeddings is demonstrated by improved correlations with clinical data and we illustrate the potential of the proposed framework in characterizing trajectories of brain development.

Conference paper

Gousias IS, Hammers A, Heckemann RA, Counsell SJ, Dyet LE, Boardman JP, Edwards AD, Rueckert Det al., 2010, Atlas selection strategy for automatic segmentation of pediatric brain MRIs into 83 ROIs, Pages: 290-293

Registration algorithms can facilitate the automatic anatomical segmentation of pediatric brain MR data sets when segmentation priors (atlases) are in hand. Automatic segmentation can be achieved through label propagation and label fusion in target space. We investigated the performance of different age cohorts used as prior atlases for the segmentation of 13 MRIs of 1-year-olds. Thirty adults and 33 2-year-olds (including the 13 1-year olds, scanned a year later) served as priors for label propagation and fusion. In addition, we tested the accuracy of a single propagation step of the atlas of the same subject scanned at 2 years of age. Pediatric priors performed better than adult priors on visual inspection as well as manual validation of the caudate nucleus (Dice index=0.89±0.02 vs. 0.86±0.03). Corresponding single atlases at the age of 2 performed better than the fusion of 30 adult priors (83 ROIs / average Dice=0.87±0.05 vs. 0.84±0.07). © 2010 IEEE.

Conference paper

Ball G, Counsell SJ, Anjari M, Merchant N, Arichi T, Doria V, Rutherford MA, Edwards AD, Rueckert D, Boardman JPet al., 2010, An optimised tract-based spatial statistics protocol for neonates: Applications to prematurity and chronic lung disease, NEUROIMAGE, Vol: 53, Pages: 94-102, ISSN: 1053-8119

Journal article

Rueckert D, Hawkes D, Gerig G, Yang G-Zet al., 2010, Special Issue on the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2009, MEDICAL IMAGE ANALYSIS, Vol: 14, Pages: 631-632, ISSN: 1361-8415

Journal article

Boardman JP, Craven C, Valappil S, Counsell SJ, Dyet LE, Rueckert D, Aljabar P, Rutherford MA, Chew ATM, Allsop JM, Cowan F, Edwards ADet al., 2010, A common neonatal image phenotype predicts adverse neurodevelopmental outcome in children born preterm, NEUROIMAGE, Vol: 52, Pages: 409-414, ISSN: 1053-8119

Journal article

Wolz R, Heckemann RA, Aljabar P, Hajnal JV, Hammers A, Lotjonen J, Rueckert Det al., 2010, Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI, NEUROIMAGE, Vol: 52, Pages: 109-118, ISSN: 1053-8119

Journal article

Rueckert D, Aljabar P, 2010, Nonrigid Registration of Medical Images: Theory, Methods, and Applications, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 27, Pages: 113-119, ISSN: 1053-5888

Journal article

Keihaninejad S, Heckemann RA, Gousias IS, Hajnal J, Duncan JS, Aljabar P, Rueckert D, Hammers Aet al., 2010, BRAIN-WIDE SURVEY OF ANATOMICAL STRUCTURES AS CLASSIFIERS IN TEMPORAL LOBE EPILEPSY USING AUTOMATIC SEGMENTATION AND STRUCTURE SELECTION, 9th European Congress on Epileptology

Poster

Heckemann RA, Keihaninejad S, Aljabar P, Rueckert D, Hajnal JV, Hammers Aet al., 2010, Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation, NEUROIMAGE, Vol: 51, Pages: 221-227, ISSN: 1053-8119

Journal article

Robinson EC, Hammers A, Ericsson A, Edwards AD, Rueckert Det al., 2010, Identifying population differences in whole-brain structural networks: A machine learning approach, NEUROIMAGE, Vol: 50, Pages: 910-919, ISSN: 1053-8119

Journal article

Keihaninejad S, Heckemann RA, Gousias IS, Hajnal JV, Duncan JS, Aljabar P, Rueckert D, Hammers Aet al., 2010, Automatic volumetry can reveal visually undetected disease features on brain MR images in temporal lobe epilepsy, ISBI 2010 (Seventh IEEE International Symposium on Biomedical Imaging)

Brain structural volumes can be used for automatically classifying subjects into categories like controls and patients. We aimed to automatically separate patients with temporal lobe epilepsy (TLE) with and without hippocampal atrophy on MRI, pTLE and nTLE, from controls, and determine the epileptogenic side. In the proposed framework 83 brain structure volumes are identified using multi-atlas segmentation. We then use structure selection using a divergence measure and classification based on structural volumes, as well as morphological similarities using SVM. A spectral analysis step is used to convert the pairwise measures of similarity between subjects into per-subject features. Up to 96% of pTLE patients were correctly separated from controls using 14 structural brain volumes. The classification method based on spectral analysis was 91% accurate at separating nTLE patients from controls. Right and left hippocampus were sufficient for the lateralization of the seizure focus in the pTLE group and achieved 100% accuracy.

Conference paper

Lotjonen JMP, Wolz R, Koikkalainen JR, Thurfjell L, Waldemar G, Soininen H, Rueckert Det al., 2010, Fast and robust multi-atlas segmentation of brain magnetic resonance images, NEUROIMAGE, Vol: 49, Pages: 2352-2365, ISSN: 1053-8119

Journal article

Wolz R, Aljabar P, Hajnal JV, Hammers A, Rueckert Det al., 2010, LEAP: Learning embeddings for atlas propagation, NEUROIMAGE, Vol: 49, Pages: 1316-1325, ISSN: 1053-8119

Journal article

Robinson EC, Rueckert D, Hammers A, Edwards ADet al., 2010, PROBABILISTIC WHITE MATTER AND FIBER TRACT ATLAS CONSTRUCTION, 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Publisher: IEEE, Pages: 1153-1156, ISSN: 1945-7928

Conference paper

Wolz R, Heckemann RA, Aljabar P, Hajnal JV, Hammers A, Lotjonen J, Rueckert Det al., 2010, MEASURING ATROPHY BY SIMULTANEOUS SEGMENTATION OF SERIAL MR IMAGES USING 4-D GRAPH-CUTS, 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Publisher: IEEE, Pages: 960-963, ISSN: 1945-7928

Conference paper

Risser L, Vialard F-X, Wolz R, Holm DD, Rueckert Det al., 2010, Simultaneous Fine and Coarse Diffeomorphic Registration: Application to Atrophy Measurement in Alzheimer's Disease, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 610-+, ISSN: 0302-9743

Conference paper

Aljabar P, Wolz R, Srinivasan L, Counsell S, Boardman JP, Murgasova M, Doria V, Rutherford MA, Edwards AD, Hajnal JV, Rueckert Det al., 2010, Combining Morphological Information in a Manifold Learning Framework: Application to Neonatal MRI, 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-+, ISSN: 0302-9743

Conference paper

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