Globally Optimal Tumor Segmentation in PET-CT Images: A Graph-Based Co-segmentation Method
Open accessAbstract
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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The contrast between the target and background in medical images is often low.
“this would be very challenging for medical images since the contrast between the target and background is often low [57].”
From DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2019) · cites this paper as [57]
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DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Wang, G., Zuluaga, M. A., Li, W., Pratt, R., Patel, P. A., Aertsen, M., Doel, T., David, A. L., Deprest, J., Ourselin, S., et al. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2840695 · Cited by 502 · see citing works
- Authors
- Han, D., Bayouth, J., Song, Q., Taurani, A., Sonka, M., Buatti, J., & Wu, X.
- Affiliations
- University of Iowa (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2011
- Volume
- 22
- Pages
- 245-256
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC3158679
- OpenAlex
- W2119173284
- Cited by
- 99
- References
- 0
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 6 · 2022: 9 · 2021: 7 · 2020: 4 · 2019: 9 · 2018: 12 · 2017: 11 · 2016: 7 · 2015: 10 · 2014: 2 · 2013: 9 · 2012: 7
- FWCI
- 4.54
- Type
- conference paper
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation
- National Institutes of Health (5r01eb004640-14)
- National Cancer Institute (K25CA123112)
- National Institute of Biomedical Imaging and Bioengineering (R01-EB004640)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- tumor segmentation, PET/CT, co-segmentation, graph-based segmentation, Markov random fields, maximum flow, multimodal image fusion, tumor delineation
- MeSH terms
- Algorithms, Artificial Intelligence, Head and Neck Neoplasms, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Pattern Recognition, Automated, Sensitivity and Specificity, Subtraction Technique, Tomography, X-Ray Computed, Reproducibility of Results, Positron-Emission Tomography
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