Cardoso, MJ;
Modat, M;
Wolz, R;
Melbourne, A;
Cash, D;
Rueckert, D;
Ourselin, S;
(2015)
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion.
IEEE Transactions on Medical Imaging
, 34
(9)
pp. 1976-1988.
10.1109/TMI.2015.2418298.
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Abstract
Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- 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.
| Type: | Article |
|---|---|
| Title: | Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMI.2015.2418298 |
| Publisher version: | http://dx.doi.org/10.1109/TMI.2015.2418298 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. |
| Keywords: | brain, image fusion, image segmentation, medical image processing, probability, visual databases, Image segmentation, Kernel, Licenses, Manifolds, Measurement, Pathology, Probabilistic logic, anatomical landmark, brain parcellation, categorical label fusion, clinical annotation, geodesic information flow, gradual information diffusion, image database, image fusion, image segmentation, large-scale morphological variability, Information propagation, label fusion, parcelation, tissue segmentation morphologically dissimilar image pathological regions-of-interest landmark probabilistic feature probabilistic tissue segmentation spatially-variant graph structure structural parcellation voxel-wise annotation voxel-wise binary |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1466784 |
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