| A fully automated human knee 3D MRI bone segmentation using the ray casting technique. | |
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MedLine Citation:
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PMID: 22038239 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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This study aimed at developing a fully automated bone segmentation method for the human knee (femur and tibia) from magnetic resonance (MR) images. MR imaging was acquired on a whole body 1.5T scanner with a gradient echo fat suppressed sequence using an extremity coil. The method was based on the Ray Casting technique which relies on the decomposition of the MR images into multiple surface layers to localize the boundaries of the bones and several partial segmentation objects being automatically merged to obtain the final complete segmentation of the bones. Validation analyses were performed on 161 MR images from knee osteoarthritis patients, comparing the developed fully automated to a validated semi-automated segmentation method, using the average surface distance (ASD), volume correlation coefficient, and Dice similarity coefficient (DSC). For both femur and tibia, respectively, data showed excellent bone surface ASD (0.50 ± 0.12 mm; 0.37 ± 0.09 mm), average oriented distance between bone surfaces within the cartilage domain (0.02 ± 0.07 mm; -0.05 ± 0.10 mm), and bone volume DSC (0.94 ± 0.05; 0.92 ± 0.07). This newly developed fully automated bone segmentation method will enable large scale studies to be conducted within shorter time durations, as well as increase stability in the reading of pathological bone. |
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Authors:
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Pierre Dodin; Johanne Martel-Pelletier; Jean-Pierre Pelletier; François Abram |
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Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-10-29 |
Journal Detail:
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Title: Medical & biological engineering & computing Volume: - ISSN: 1741-0444 ISO Abbreviation: - Publication Date: 2011 Oct |
Date Detail:
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Created Date: 2011-10-31 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7704869 Medline TA: Med Biol Eng Comput Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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ArthroVision, 1871 Sherbrooke Street East, Montreal, QC, H2K 1B6, Canada. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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