ArticleIEEE transactions on radiation and plasma medical sciences2020
Variational PET/CT Tumor Co-segmentation Integrated with PET Restoration.
Article in IEEE transactions on radiation and plasma medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Multitask connected U-Net: automatic lung cancer segmentation from CT images using PET knowledge guidance.Frontiers in artificial intelligence · 2024Article
- Joint EANM/SNMMI/ESTRO practice recommendations for the use of 2-[European journal of nuclear medicine and molecular imaging · 2022Review
- Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting.European journal of nuclear medicine and molecular imaging · 2021Article
- Transcriptomics in cancer revealed by Positron Emission Tomography radiomics.Scientific reports · 2020Article
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3 authors.
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Abstract
PET and CT are widely used imaging modalities in radiation oncology. PET imaging has a high contrast but blurry tumor edges due to its limited spatial resolution, while CT imaging has a high resolution but a low contrast between tumor and soft normal tissues. Tumor segmentation from either a single PET or CT image is difficult. It is known that co-segmentation methods utilizing the complementary information between PET and CT can improve segmentation accuracy. These information can be either consistent or inconsistent in the image-level. How to correctly localize tumor edges with these inconsistent information is a major challenge for co-segmentation methods. In this study, we proposed a novel variational method for tumor co-segmentation in PET/CT, with a fusion strategy specifically designed to handle the information inconsistency between PET and CT in an adaptive way - the method can automatically decide which modality should be more trustful when PET and CT disagree to each other for localizing the tumor boundary. The proposed method was constructed based on the
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