The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion
Open accessAbstract
Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regions-of-interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability.
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Medical Image Computing and Computer-Assisted Intervention
Yang, G., Hawkes, D. J., Rueckert, D., Noble, J. A., & Taylor, C. (2009) · Research Explorer (The University of Manchester) · Cited by 1,591
Self-citationFast free-form deformation using graphics processing units
Modat, M., Ridgway, G. R., Taylor, Z. A., Lehmann, M., Barnes, J., Hawkes, D. J., Fox, N. C., & Ourselin, S. (2010) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2009.09.002 · Cited by 1,085
Self-citationAutomated model-based tissue classification of MR images of the brain
Van Leemput, K., Maes, F., Vandermeulen, D., & Suetens, P. (1999) · IEEE Transactions on Medical Imaging · doi:10.1109/42.811270 · Cited by 1,024
Diffusion Kernels on Graphs and Other Discrete Input Spaces
Kondor, R., & Lafferty, J. (2002) · Cited by 725
“Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images
Bookstein, F. L. (2001) · NeuroImage · doi:10.1006/nimg.2001.0770 · Cited by 664
Davatzikos, C. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.05.010 · Cited by 434
Nonparametric Scene Parsing via Label Transfer
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Metadata
- Authors
- Cardoso, M. J., Modat, M., Wolz, R., Melbourne, A., Cash, D., Rueckert, D., & Ourselin, S.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2015-09
- Volume
- 34
- Issue
- 9
- Pages
- 1976-1988
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2054422278
- Cited by
- 344
- References
- 30
- FWCI
- 19.06
- Type
- journal article
- Language
- en
- Open access
- hybrid
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