Prior-based Coregistration and Cosegmentation

Abstract : We propose a modular and scalable framework for dense coregistration and cosegmentation with two key characteristics: first, we substitute ground truth data with the semantic map output of a classifier; second, we combine this output with population deformable registration to improve both alignment and segmentation. Our approach deforms all volumes towards consensus, taking into account image similarities and label consistency. Our pipeline can incorporate any classifier and similarity metric. Results on two datasets, containing annotations of challenging brain structures, demonstrate the potential of our method.
Type de document :
Communication dans un congrès
MICCAI 2016, 2016, Athens, Greece. 2016
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Contributeur : Enzo Ferrante <>
Soumis le : mardi 26 juillet 2016 - 21:43:30
Dernière modification le : mardi 5 février 2019 - 13:52:14

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  • HAL Id : hal-01349189, version 1
  • ARXIV : 1607.06787


Mahsa Shakeri, Enzo Ferrante, Stavros Tsogkas, Sarah Lippe, Samuel Kadoury, et al.. Prior-based Coregistration and Cosegmentation. MICCAI 2016, 2016, Athens, Greece. 2016. 〈hal-01349189〉



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