| Prior Shape Level Set Segmentation on Multi-Step Generated Probability Maps of MR Datasets for Fully Automatic Kidney Parenchyma Volumetry. | |
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MedLine Citation:
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PMID: 21937343 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Fully automatic 3D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects and similar tissue properties of adjacent tissues. We developed a 3D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation. The probability map quality is improved in a multi-step refinement approach. An extended prior shape level set segmentation method is then applied on the refined probability maps. The segmentation quality is improved by incorporating an exterior cortex edge alignment technique using cortex probability maps. In contrast to previous approaches we combine several relevant kidney parenchyma features in a sequence of segmentation techniques for successful parenchyma delineation on native MR datasets. Furthermore, the proposed method is able to recognize and exclude parenchymal cysts from the parenchymal volume. We analyzed four different quality measures showing better results for right parenchymal tissue than for left parenchymal tissue due to an incorporated liver part removal in the segmentation framework. The results show that the outer cortex edge alignment approach successfully improves the quality measures. |
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Authors:
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O Gloger; K D Tonnies; V Liebscher; B Kugelmann; R Laqua; H Voelzke |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-9-19 |
Journal Detail:
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Title: IEEE transactions on medical imaging Volume: - ISSN: 1558-0062 ISO Abbreviation: - Publication Date: 2011 Sep |
Date Detail:
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Created Date: 2011-9-22 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8310780 Medline TA: IEEE Trans Med Imaging Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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