| Zonal segmentation of prostate using multispectral magnetic resonance images. | |
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MedLine Citation:
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PMID: 22047374 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Purpose: To investigate the performance of a new method of automatic segmentation of prostatic multispectral magnetic resonance images into two zones: the peripheral zone and the central gland.Methods: The proposed method is based on a modified version of the evidential C-means clustering algorithm. The evidential C-means optimization process was modified to introduce spatial neighborhood information. A priori knowledge of the prostate's zonal morphology was modeled as a geometric criterion and used as an additional data source to enhance the differentiation of the two zones.Results: Thirty-one clinical magnetic resonance imaging series were used to validate the method, and interobserver variability was taken into account in assessing its accuracy. The mean Dice Similarity Coefficient was 89% for the central gland and 80% for the peripheral zone, as validated by a consensus from expert radiologist segmentation.Conclusions: The method was statistically insensitive to variations in patient age, prostate volume and the presence of tumors, which increases its feasibility in a clinical context. |
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Authors:
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N Makni; A Iancu; O Colot; P Puech; S Mordon; N Betrouni |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Medical physics Volume: 38 ISSN: 0094-2405 ISO Abbreviation: Med Phys Publication Date: 2011 Nov |
Date Detail:
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Created Date: 2011-11-03 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0425746 Medline TA: Med Phys Country: United States |
Other Details:
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Languages: eng Pagination: 6093 Citation Subset: IM |
Affiliation:
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Inserm, U703, Université Nord de France and LAGIS, CNRS FRE-CNRS 3303, 152 rue du Docteur Yersin 59120 Loos Villeneuve d'Ascq, 59655, France. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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