| pubmed-article:17633686 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C0205177 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C0441889 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C0036849 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C0444504 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C1442518 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C1552652 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C1552685 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C1705195 | lld:lifeskim |
| pubmed-article:17633686 | lifeskim:mentions | umls-concept:C1521738 | lld:lifeskim |
| pubmed-article:17633686 | pubmed:dateCreated | 2007-7-18 | lld:pubmed |
| pubmed-article:17633686 | pubmed:abstractText | We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures. | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:grant | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:language | eng | lld:pubmed |
| pubmed-article:17633686 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:17633686 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:17633686 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:17633686 | pubmed:issn | 1011-2499 | lld:pubmed |
| pubmed-article:17633686 | pubmed:author | pubmed-author:KikinisRonR | lld:pubmed |
| pubmed-article:17633686 | pubmed:author | pubmed-author:WellsWilliam... | lld:pubmed |
| pubmed-article:17633686 | pubmed:author | pubmed-author:PohlKilian... | lld:pubmed |
| pubmed-article:17633686 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:17633686 | pubmed:volume | 20 | lld:pubmed |
| pubmed-article:17633686 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:17633686 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:17633686 | pubmed:pagination | 26-37 | lld:pubmed |
| pubmed-article:17633686 | pubmed:dateRevised | 2011-9-22 | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:meshHeading | pubmed-meshheading:17633686... | lld:pubmed |
| pubmed-article:17633686 | pubmed:year | 2007 | lld:pubmed |
| pubmed-article:17633686 | pubmed:articleTitle | Active mean fields: solving the mean field approximation in the level set framework. | lld:pubmed |
| pubmed-article:17633686 | pubmed:affiliation | Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA. pohl@bwh.harvard.edu | lld:pubmed |
| pubmed-article:17633686 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:17633686 | pubmed:publicationType | Research Support, U.S. Gov't, Non-P.H.S. | lld:pubmed |
| pubmed-article:17633686 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
| pubmed-article:17633686 | pubmed:publicationType | Evaluation Studies | lld:pubmed |
| pubmed-article:17633686 | pubmed:publicationType | Research Support, N.I.H., Extramural | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:17633686 | lld:pubmed |
