Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Template-Cut: A Pattern-based Segmentation Paradigm

Institution:
1Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
3Department of Neurosurgery, University of Marburg, Marburg, Germany.
Publisher:
Nature Publishing Group (NPG)
Publication Date:
May-2012
Journal:
Nature - Scientific Reports, Nature Publishing Group (NPG)
Volume Number:
420
Issue Number:
2
Pages:
1-8
Citation:
Sci Rep. 2012 May;2:420:1-8.
Links:
http://www.nature.com/srep/2012/120524/srep00420/full/srep00420.html
PubMed ID:
22639728
PMCID:
PMC3359527
Appears in Collections:
NCIGT, NA-MIC, SPL
Sponsors:
P41 RR019703/RR/NCRR NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
R03 EB013792/EB/NIBIB NIH HHS/United States
Generated Citation:
Egger J., Freisleben B., Nimsky C., Kapur T. Template-Cut: A Pattern-based Segmentation Paradigm. Sci Rep. 2012 May;2:420:1-8. PMID: 22639728. PMCID: PMC3359527.
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We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.

Additional Material
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Egger-SciRep2012-fig6.jpg (325.261kB)