Research Topics
| Robin WolzSummaryAffiliation: Imperial College Country: UK Publications
| Collaborators
|
Detail Information
Publications
Nonlinear dimensionality reduction combining MR imaging with non-imaging informationRobin Wolz
Medical Image Analysis Group, Department of Computing, Imperial College London, 180 Queen s Gate, London SW7 2AZ, UK
Med Image Anal 16:819-30. 2012..Our classification results compare favorably to what has been reported in a recent meta-analysis using established neuroimaging methods on the same database...
LEAP: learning embeddings for atlas propagationRobin Wolz
Visual Information Processing Group, Department of Computing, Imperial College London, 180 Queen s Gate, London, SW7 2AZ, UK
Neuroimage 49:1316-25. 2010..with a greater difference between atlas and image. For the segmentation of the hippocampus on 182 images for which a manual segmentation is available, we achieved an average overlap (Dice coefficient) of 0.85 with the manual reference...
Measurement of hippocampal atrophy using 4D graph-cut segmentation: application to ADNIRobin Wolz
Department of Computing, Imperial College London, London, UK
Neuroimage 52:109-18. 2010..Power analysis shows that 67 and 206 subjects are needed for the AD and MCI groups respectively to detect a 25% change in volume loss with 80% power and 5% significance...
Simultaneous multi-scale registration using large deformation diffeomorphic metric mappingLaurent Risser
Institute for Mathematical Science, Imperial College, SW7 2PG, London, UK
IEEE Trans Med Imaging 30:1746-59. 2011..Results show that our method registers accurately volumetric images containing feature differences at several scales simultaneously with smooth deformations...
Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's diseaseKatherine R Gray
Biomedical Image Analysis Group, Department of Computing, Imperial College London, UK
Neuroimage 60:221-9. 2012..This finding may be usefully applied in the diagnosis of Alzheimer's disease, predicting disease course in individuals with mild cognitive impairment, and in the selection of participants for clinical trials...
Hierarchical manifold learningKanwal K Bhatia
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
Med Image Comput Comput Assist Interv 15:512-9. 2012..settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,..
Simultaneous fine and coarse diffeomorphic registration: application to atrophy measurement in Alzheimer's diseaseLaurent Risser
Institute for Mathematical Science, Imperial College London, 53 Prince s Gate, SW7 2PG London, UK
Med Image Comput Comput Assist Interv 13:610-7. 2010..More importantly, the results also demonstrate its ability to measure shape variations at several scales simultaneously while keeping the desirable properties of LDDMM. This opens new perspectives for clinical applications...
Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlasesRobin Wolz
Imperial College London, London, UK
Med Image Comput Comput Assist Interv 15:10-7. 2012..Our results on a dataset of 100 CT scans compare favourable to the state-of-the-art with Dice overlap values of 94%, 91%, 66% and 94% for liver, spleen, pancreas and kidney respectively...
Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's diseaseMaria Vounou
Statistics Section, Department of Mathematics, Imperial College London, UK
Neuroimage 60:700-16. 2012..Our findings confirmed the key role of the APOE and TOMM40 genes but also highlighted some novel potential associations with AD...
Multi-method analysis of MRI images in early diagnostics of Alzheimer's diseaseRobin Wolz
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
PLoS ONE 6:e25446. 2011..The most stable and reliable classification was achieved when combining all available features...
