| Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method. | |
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MedLine Citation:
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PMID: 21761661 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Tumor segmentation in PET and CT images is notoriously challenging due to the low spatial resolution in PET and low contrast in CT images. In this paper, we have proposed a general framework to use both PET and CT images simultaneously for tumor segmentation. Our method utilizes the strength of each imaging modality: the superior contrast of PET and the superior spatial resolution of CT. We formulate this problem as a Markov Random Field (MRF) based segmentation of the image pair with a regularized term that penalizes the segmentation difference between PET and CT. Our method simulates the clinical practice of delineating tumor simultaneously using both PET and CT, and is able to concurrently segment tumor from both modalities, achieving globally optimal solutions in low-order polynomial time by a single maximum flow computation. The method was evaluated on clinically relevant tumor segmentation problems. The results showed that our method can effectively make use of both PET and CT image information, yielding segmentation accuracy of 0.85 in Dice similarity coefficient and the average median hausdorff distance (HD) of 6.4 mm, which is 10% (resp., 16%) improvement compared to the graph cuts method solely using the PET (resp., CT) images. |
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Authors:
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Dongfeng Han; John Bayouth; Qi Song; Aakant Taurani; Milan Sonka; John Buatti; Xiaodong Wu |
Publication Detail:
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Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
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Title: Information processing in medical imaging : proceedings of the ... conference Volume: 22 ISSN: 1011-2499 ISO Abbreviation: Inf Process Med Imaging Publication Date: 2011 |
Date Detail:
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Created Date: 2011-07-18 Completed Date: 2011-08-31 Revised Date: 2011-09-26 |
Medline Journal Info:
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Nlm Unique ID: 9216871 Medline TA: Inf Process Med Imaging Country: Germany |
Other Details:
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Languages: eng Pagination: 245-56 Citation Subset: IM |
Affiliation:
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Department of Radiation Oncology, The University of Iowa, Iowa City, IA, USA. handongfeng@gmail.com |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms* Artificial Intelligence Head and Neck Neoplasms / diagnosis* Humans Image Enhancement / methods Image Interpretation, Computer-Assisted / methods* Pattern Recognition, Automated / methods* Positron-Emission Tomography / methods* Reproducibility of Results Sensitivity and Specificity Subtraction Technique* Tomography, X-Ray Computed / methods* |
| Grant Support | |
ID/Acronym/Agency:
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K25 CA123112/CA/NCI NIH HHS; K25 CA123112-04/CA/NCI NIH HHS; R01 EB004640/EB/NIBIB NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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