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rdf:type |
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lifeskim:mentions |
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pubmed:issue |
Pt 2
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pubmed:dateCreated |
2010-4-29
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pubmed:abstractText |
Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion. By capitalizing on recently developed fast and robust pairwise image alignment tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white matter, cortex and subcortical structures. SNIP yields better segmentation than state-of-the-art algorithms in multiple regions of interest.
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pubmed:grant |
http://linkedlifedata.com/resource/pubmed/grant/P41 RR013218-030002,
http://linkedlifedata.com/resource/pubmed/grant/P41 RR014075-086765,
http://linkedlifedata.com/resource/pubmed/grant/P41-RR13218,
http://linkedlifedata.com/resource/pubmed/grant/P41-RR14075,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB001550,
http://linkedlifedata.com/resource/pubmed/grant/R01 EB001550-02,
http://linkedlifedata.com/resource/pubmed/grant/R01 NS051826-03,
http://linkedlifedata.com/resource/pubmed/grant/R01 NS052585-01,
http://linkedlifedata.com/resource/pubmed/grant/R01 NS052585-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R01 RR016594-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R01 RR16594-01A1,
http://linkedlifedata.com/resource/pubmed/grant/R01-NS051826,
http://linkedlifedata.com/resource/pubmed/grant/R01EB006758,
http://linkedlifedata.com/resource/pubmed/grant/U24 RR021382-02,
http://linkedlifedata.com/resource/pubmed/grant/U24-RR021382,
http://linkedlifedata.com/resource/pubmed/grant/U54 EB005149-03,
http://linkedlifedata.com/resource/pubmed/grant/U54 EB005149-050001,
http://linkedlifedata.com/resource/pubmed/grant/U54-EB005149
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pubmed:commentsCorrections |
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:status |
MEDLINE
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pubmed:author |
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pubmed:volume |
12
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
1075-83
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pubmed:dateRevised |
2011-9-26
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pubmed:meshHeading |
pubmed-meshheading:20426218-Algorithms,
pubmed-meshheading:20426218-Artificial Intelligence,
pubmed-meshheading:20426218-Brain,
pubmed-meshheading:20426218-Humans,
pubmed-meshheading:20426218-Image Enhancement,
pubmed-meshheading:20426218-Image Interpretation, Computer-Assisted,
pubmed-meshheading:20426218-Magnetic Resonance Imaging,
pubmed-meshheading:20426218-Nerve Fibers, Myelinated,
pubmed-meshheading:20426218-Pattern Recognition, Automated,
pubmed-meshheading:20426218-Reproducibility of Results,
pubmed-meshheading:20426218-Sensitivity and Specificity
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pubmed:year |
2009
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pubmed:articleTitle |
Supervised nonparametric image parcellation.
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pubmed:affiliation |
Computer Science and Artificial Intelligence Lab, MIT, USA.
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, Non-P.H.S.,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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