Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion

Chunfeng Lian, Hua Li, Pierre Vera, Su Ruan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages220-223
Number of pages4
ISBN (Electronic)9781538636367
DOIs
StatePublished - May 23 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: Apr 4 2018Apr 7 2018

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2018-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Country/TerritoryUnited States
CityWashington
Period04/4/1804/7/18

Keywords

  • Belief Functions
  • Clustering
  • Information Fusion
  • PET-CT
  • Tumor Co-Segmentation

Fingerprint

Dive into the research topics of 'Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion'. Together they form a unique fingerprint.

Cite this