Abstract:
:In this paper, we present a graph-based concurrent brain tumor segmentation and atlas to diseased patient registration framework. Both segmentation and registration problems are modeled using a unified pairwise discrete Markov Random Field model on a sparse grid superimposed to the image domain. Segmentation is addressed based on pattern classification techniques, while registration is performed by maximizing the similarity between volumes and is modular with respect to the matching criterion. The two problems are coupled by relaxing the registration term in the tumor area, corresponding to areas of high classification score and high dissimilarity between volumes. In order to overcome the main shortcomings of discrete approaches regarding appropriate sampling of the solution space as well as important memory requirements, content driven samplings of the discrete displacement set and the sparse grid are considered, based on the local segmentation and registration uncertainties recovered by the min marginal energies. State of the art results on a substantial low-grade glioma database demonstrate the potential of our method, while our proposed approach shows maintained performance and strongly reduced complexity of the model.
journal_name
Med Image Analjournal_title
Medical image analysisauthors
Parisot S,Wells W 3rd,Chemouny S,Duffau H,Paragios Ndoi
10.1016/j.media.2014.02.006subject
Has Abstractpub_date
2014-05-01 00:00:00pages
647-59issue
4eissn
1361-8415issn
1361-8423pii
S1361-8415(14)00029-2journal_volume
18pub_type
杂志文章abstract::This paper describes a method for building efficient representations of large sets of brain images. Our hypothesis is that the space spanned by a set of brain images can be captured, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifol...
journal_title:Medical image analysis
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