Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 34, Issue 9, Pages 1976-1988Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2015.2418298
Keywords
Information propagation; label fusion; parcelation; tissue segmentation
Categories
Funding
- EPSRC [EP/H046410/1, EP/J020990/1, EP/K005278]
- MRC [MR/J01107X/1]
- EU-FP7 project VPH-DARE@IT [FP7-ICT-2011-9-601055]
- NIHR Biomedical Research Unit (Dementia) at UCL
- National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative) [BW.mn.BRC10269]
- UCL Leonard Wolfson Experimental Neurology Centre [PR/ylr/18575]
- Brain Research Trust
- UK registered charity SPARKS
- 7th Framework Programme by the European Commission
- EPSRC [EP/J020990/1, EP/H046410/1] Funding Source: UKRI
- MRC [MR/J01107X/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/H046410/1, EP/J020990/1] Funding Source: researchfish
- Medical Research Council [MR/J01107X/1] Funding Source: researchfish
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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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