Abstract：
:Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Sjöberg C,Ahnesjö Adoi
10.1016/j.cmpb.2012.12.006subject
Has Abstractpub_date
2013-06-01 00:00:00pages
308-19issue
3eissn
0169-2607issn
1872-7565pii
S0169-2607(13)00002-3journal_volume
110pub_type
杂志文章abstract：BACKGROUND:Structural changes of the brain's third ventricle have been acknowledged as an indicative measure of the brain atrophy progression in neurodegenerative and endocrinal diseases. To investigate the ventricular enlargement in relation to the atrophy of the surrounding structures, shape analysis is a promising a...
journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/0169-2607(93)90063-q
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journal_title：Computer methods and programs in biomedicine
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pub_type： 临床试验,杂志文章
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/j.cmpb.2008.04.007
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/s0169-2607(98)00064-9
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/j.cmpb.2007.07.009
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/j.cmpb.2009.09.005
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journal_title：Computer methods and programs in biomedicine
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doi：10.1016/j.cmpb.2010.06.012
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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更新日期：2019-07-01 00:00:00
