@inProceedings{
 title = {Uncertainty-driven efficiently-sampled sparse graphical models for concurrent tumor segmentation and atlas registration},
 type = {inProceedings},
 year = {2013},
 pages = {641-648},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 id = {975d66b1-7d0d-3c35-ba06-a90a3d0065a8},
 created = {2015-09-08T00:08:17.000Z},
 file_attached = {true},
 profile_id = {c08cee0c-cdfe-3253-a183-89b4a1ce8187},
 last_modified = {2015-09-08T00:15:13.000Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 abstract = {Graph-based methods have become popular in recent years and have \nsuccessfully addressed tasks like segmentation and deformable registration. \nTheir main strength is optimality of the obtained solution while their main \nlimitation is the lack of precision due to the grid-like representations and the \ndiscrete nature of the quantized search space. In this paper we introduce a \nnovel approach for combined segmentation/registration of brain tumors that \nadapts graph and sampling resolution according to the image content. To this end \nwe estimate the segmentation and registration marginals towards adaptive graph \nresolution and intelligent definition of the search space. This information is \nconsidered in a hierarchical framework where uncertainties are propagated in a \nnatural manner. State of the art results in the joint segmentation/registration \nof brain images with low-grade gliomas demonstrate the potential of our \napproach.},
 bibtype = {inProceedings},
 author = {Parisot, Sarah and Wells, William and Chemouny, Stephane and Duffau, Hugues and Paragios, Nikos},
 booktitle = {Proceedings of the IEEE International Conference on Computer Vision}
}

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