Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

Qi Song; Junjie Bai; Dongfeng Han; Sudershan Bhatia; Wenqing Sun; William Rockey; John E Bayouth; John M Buatti; Xiaodong Wu

doi:10.1109/TMI.2013.2263388

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Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

Journal article

Peer reviewed

Optimal Co-segmentation of Tumor in PET-CT Images with Context Information

Qi Song, Junjie Bai, Dongfeng Han, Sudershan Bhatia, Wenqing Sun, William Rockey, John E Bayouth, John M Buatti and Xiaodong Wu

IEEE transactions on medical imaging, Vol.32(9), pp.1685-1697

09/2013

DOI: 10.1109/TMI.2013.2263388

PMCID: PMC3965345

PMID: 23693127

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Abstract

PET-CT images have been widely used in clinical practice for radiotherapy treatment planning of the radiotherapy. Many existing segmentation approaches only work for a single imaging modality, which suffer from the low spatial resolution in PET or low contrast in CT. In this work we propose a novel method for the co-segmentation of the tumor in both PET and CT images, which makes use of advantages from each modality: the functionality information from PET and the anatomical structure information from CT. The approach formulates the segmentation problem as a minimization problem of a Markov Random Field (MRF) model, which encodes the information from both modalities. The optimization is solved using a graph-cut based method. Two sub-graphs are constructed for the segmentation of the PET and the CT images, respectively. To achieve consistent results in two modalities, an adaptive context cost is enforced by adding context arcs between the two subgraphs. An optimal solution can be obtained by solving a single maximum flow problem, which leads to simultaneous segmentation of the tumor volumes in both modalities. The proposed algorithm was validated in robust delineation of lung tumors on 23 PET-CT datasets and two head-and-neck cancer subjects. Both qualitative and quantitative results show significant improvement compared to the graph cut methods solely using PET or CT.

PET-CT

graph cut

lung tumor

image segmentation

global optimization

context information

Details

Title: Subtitle: Optimal Co-segmentation of Tumor in PET-CT Images with Context Information
Creators: Qi Song - Biomedical Image Analysis Lab, GE Global Research Center, Niskayuna, NY 12309, USA. The work was mainly finished when he was with the Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
Junjie Bai - Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
Dongfeng Han - Department of Electrical & Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA
Sudershan Bhatia - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
Wenqing Sun - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
William Rockey - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
John E Bayouth - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
John M Buatti - Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
Xiaodong Wu - Department of Electrical & Computer Engineering and the Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA
Resource Type: Journal article
Publication Details: IEEE transactions on medical imaging, Vol.32(9), pp.1685-1697
DOI: 10.1109/TMI.2013.2263388
PMID: 23693127
PMCID: PMC3965345
NLM abbreviation: IEEE Trans Med Imaging
ISSN: 0278-0062
eISSN: 1558-254X
Publisher: Institute of Electrical and Electronics Engineers
Language: English
Date published: 09/2013
Academic Unit: Electrical and Computer Engineering; Radiation Oncology; Neurosurgery; Otolaryngology
Record Identifier: 9984040395602771

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