Name
Title | ||
|---|---|---|
Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion |
Abstract | ||
|---|---|---|
Accurate segmentation of target tumor is a precondition for effective radiation therapy. While hybrid positron emission tomography-computed tomography (PET-CT) has become a standard imaging tool in the practical process of radiation oncology, many existing segmentation methods are still performed in mono-modalities. We propose an automatic 3-D method based on unsupervised learning to jointly delineate tumor contours in PET-CT images, considering that the two distinct modalities can provide each other complementary information so as to improve segmentation. As PET-CT images are noisy and blurry, the theory of belief functions is adopted to model the uncertain and imprecise image information, and to fuse them in a stable way. To ensure reliable clustering in each modality, an adaptive distance metric to quantify distortions is proposed, and the spatial information is taken into account. A novel context term is designed to encourage consistent segmentation between the two modalities. In addition, during the iterative process of unsupervised learning, a specific fusion strategy is applied to further adjust results for the two distinct modalities. The proposed co-segmentation method has been evaluated by fifteen PET-CT images for non-small cell lung cancer (NSCLC) patients, showing good performance compared to some other methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363559 | 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) |
Keywords | Field | DocType |
Tumor Co-Segmentation,Information Fusion,Clustering,Belief Functions,PET-CT | Spatial analysis,Modalities,Computer vision,PET-CT,Iterative and incremental development,Pattern recognition,Computer science,Segmentation,Metric (mathematics),Unsupervised learning,Artificial intelligence,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-5386-3637-4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunfeng Lian | 1 | 132 | 22.61 |
| Hua Li | 2 | 45 | 9.03 |
| Pierre Vera | 3 | 59 | 10.15 |
| Ruan Su | 4 | 559 | 53.00 |