Conference proceeding
Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method
Information processing in medical imaging : proceedings of the ... conference, Vol.22, pp.245-256
2011
DOI: 10.1007/978-3-642-22092-0_21
PMCID: PMC3158679
PMID: 21761661
Abstract
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.
Details
- Title: Subtitle
- Globally optimal tumor segmentation in PET-CT images: a graph-based co-segmentation method
- Creators
- Dongfeng Han - Department of Radiation Oncology, The University of Iowa, Iowa City, IA, USA. handongfeng@gmail.comJohn BayouthQi SongAakant TauraniMilan SonkaJohn BuattiXiaodong Wu
- Resource Type
- Conference proceeding
- Publication Details
- Information processing in medical imaging : proceedings of the ... conference, Vol.22, pp.245-256
- DOI
- 10.1007/978-3-642-22092-0_21
- PMID
- 21761661
- PMCID
- PMC3158679
- NLM abbreviation
- Inf Process Med Imaging
- ISSN
- 1011-2499
- Publisher
- Germany
- Grant note
- R01 EB004640 / NIBIB NIH HHS K25 CA123112-04 / NCI NIH HHS K25 CA123112 / NCI NIH HHS
- Language
- English
- Date published
- 2011
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Injury Prevention Research Center; Neurosurgery; Otolaryngology; Ophthalmology and Visual Sciences
- Record Identifier
- 9984040473702771
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